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Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance
Abstract
In the era of globalization, student placement is very challenging issue for all educational institutions. For engineering institutions, placement is a key factor to maintain good ranking in the university as well as in other national and international ranking agencies. In this paper, we have proposed a few supervised machine learning classifiers which may be used to predict the placement of a student in the IT industry based on their academic performance in class Tenth, Twelve, Graduation, and Backlog till date in Graduation. We also compare the results of different proposed classifiers. Various parameters used to compare and analyze the results of different developed classifiers are accuracy score, percentage accuracy score, confusion matrix, heatmap, and classification report. Classification report generated by developed classifiers consists of parameters precision, recall, f1-score, and support. The classification algorithms Support Vector Machine, Gaussian Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Stochastic Gradient Descent, Logistic Regression, and Neural Network are used to develop the classifiers. All the developed classifiers are also tested on new data which are excluded from the dataset used in the experiment.
... Machine learning can be described as a scientific field that uses computer science and statistics to make computers lean without an explicit programs (Maurya, Hussain, & Singh, 2021). To develop an algorithm that is able to predict, machine learning techniques employs part of the data to develop such algorithms. ...
Early diagnosis of Parkinson's disease has a significant impact on patient's lives as it is described as a disease in the nervous system of the human body, which leads to disorders in some parts of body. The paper aims at employing a human gait character, i.e. stride time interval, along with the support vector machine (SVM), for classifying patients who suffer Parkinson's disease (PD). To conduct the classification task, the support vector machine classifier was employed on data taken from several human individuals. The data consist of stride time interval records of both normal people and PD patients. The findings of the current study prove the usefulness of using a machine learning technique along with a human gait character to identify Parkinson's disease patients. The current results show that SVM classifier achieved nearly 90% accuracy.
... In order to predict a student's placement in the IT business based on their academic achievement in class ten, class twelve, graduation, and backlog to date in graduation, Maurya et al. [13] have presented a few supervised machine learning classifiers. ...
Students learning performance is one of the core components for assessing any educational systems. Students performance is very crucial in tackling issues of learning process and one of the important matters to measure learning outcomes. The ability to use data knowledge to improve education systems has led to the development of the field of research known as educational data mining (EDM). EDM is the creation of techniques to investigate data gathered from educational settings, allowing for a more thorough and accurate understanding of students and the improvement of educational outcomes for them. The use of machine learning (ML) technology has increased significantly in recent years. Researchers and teachers can use the measurements of success, failure, dropout, and more provided by the discipline of data mining in education to predict and simulate education processes. Therefore, this work presents an analysis of students performance using data mining methods. The paper presents both clustering and classification techniques to identify the impact of students performance at early stage with on the GPA. For the clustering technique, the paper uses dimensionality reduction mechanism by T-SNE algorithm with various factors at early stage such as admission scores and first level courses, academic achievement tests (AAT) and general aptitude tests (GAT) in order to explore the relationship between these factors and GPA’s. For the classification technique, the paper presents experiments on different machine learning models on predicting student performance at early stages using different features including courses’ grades and admission tests’ scores. We use different assessment metrics to evaluate the quality of the models. The results suggest that educational systems can mitigate the risks of students failures at the early stages.
... By analyzing the connotations, influencing factors, and cultivation principles of the active learning attitude, we can know that there're obvious differences in the learning attitude of English majors of different genders and with different learning experiences [5][6][7][8][9]. To attain higher teaching quality, adopting both quantitative and qualitative methods to discuss the mechanism of how active learning attitude influences the classified training quality of English majors is of practical meaning [10][11][12][13][14][15][16][17][18]. ...
To attain higher teaching quality, adopting both quantitative and qualitative methods to discuss the mechanism of how active learning attitude influences the classified training quality of English majors is of practical meaning. However, few existing studies have concerned about this research topic, so to fill in this research blank, this paper aims to study the prediction, monitoring, and management of the classified training quality of English major students. At first, this paper analyzed the classified training of English majors, and introduced the factors of the cultivation of active learning attitude in English majors and the typical manifestations of English majors with an active learning attitude. Then, this paper used the comprehensive improvement level of each evaluation index to measure the classified training quality, and combined the Grey Wolf Optimizer (GWO) with the Support Vector Machine (SVM) to propose a model for predicting the classified training quality of English majors, which laid a basis for further quality monitoring and management. At last, the experimental results verified the effectiveness of using the constructed model to predict the classified training quality of English majors, and gave a few suggestions for monitoring and managing the classified training of English majors.
... Multidimensional data fusion is the premise of applying big data technology in specific fields, and early behavioral data fusion methods mainly focus on semantics [19] and similarity [20]. Inspired by the multisource data fusion theory, many researchers have integrated various data sources on campus, including data on motion trajectories, student learning, student consumption, and classroom attendance, and used support vector machines (SVM) and machine learning (ML) classifiers, etc. to predict student course grade [21][22][23]. Student multidimensional information data fusion can fuse the data generated by different data sources to mine the student behavior rules more comprehensively [24]. Due to the dynamic, complex, and multidimensional nature of student behavior data, the identification of abnormal student behavior groups cannot simply use a single-dimensional perspective. ...
With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.
... Student grades are common and recognised data, easy to obtain and understand. They are spotlighted because of the number of publications that used and described them as accessible and manageable (Maurya et al., 2021;Namoun & Alshanqiti, 2021;Rastrollo-Guerrero et al., 2020;BuenañoFernández et al., 2019). These data allow the easy implementation of several analytic operations as recommended practices for AI applications (PFA Community, 2015). ...
This work objective is to describe two experiments using student grades of an online higher education program to build and apply three classifiers to predict student performance. In the literature, the three classifiers, a Probabilistic Neural Network, a Support Vector Machine, and a Discriminant Analysis, have proved efficient. I applied the leave-one-out cross-validation method, tested their performances by five criteria, and compared their results through statistical analysis. The analyses of the five performance criteria support the decision on which model applies given particular prediction goals. The results allow timely identification of students at risk of failure for early intervention and predict which students will succeed.
Find it here: https://link.springer.com/article/10.1007/s10639-022-11106-4
Student placement plays a crucial role in educational institutions, as it is a factor measure to identify the performance of an institution. Timely identification of student’s CGPA range can reduce concerns about students’ academic life and it will help to improve success and avoid risks. Recently, there has been an upsurge in the use of machine learning techniques for prediction, this research focuses on random forest for student prediction. This paper focus on the prediction of student placements based on criteria like attendance, backlog, communication, programming skill, verbal and aptitude exam over more than 500 samples, then the PCA technique is used to reduce the dimensionality of the dataset and to pinpoint the key elements that influence the prediction of student placement.The most crucial elements that influence a student’s likelihood of finding employment after completing their studies can be determined. Used PCA to identify the most important features that contribute to the probability of pupil placement. PCA works by modifying the initial dataset to a fresh set of variables, and principal elements, which identify the most significant patterns and variations in the data to increase the accuracy. The random forest algorithm is then used to build a predictive model and achieved 97 percentage of accuracy,that is trained on the reduced feature set to Identify the probability of a student being assigned to a job.By simplifying the dataset and focusing on the most important factors that affect the prediction, this method has been demonstrated to increase the efficiency and accuracy of student placement prediction. It is crucial to remember that the Random Forest model’s accuracy depends on the calibre and relevance of the input data. Additionally, it’s crucial to make sure the model is applied ethically, fairly, and without any prejudice or discrimination.
This study investigates the effectiveness of machine learning and deep learning models for early prediction of student performance in higher education institutions. Using the Open University Learning Analytics (OULA) dataset, various models, including Decision Tree, Support Vector Machine, Neural Network, and Ensemble Model, were employed to predict student performance in three categories: Pass/Fail, Close to Fail, and Close to Pass. The Ensemble Model (EM) consistently outperformed other models, achieving the highest overall F1 measure, precision, recall, and accuracy. These results highlight the potential of data-driven techniques in informing educational stakeholders’ decision-making processes, enabling targeted interventions, and facilitating personalized learning strategies tailored to students’ needs. By identifying at-risk students early in the academic year, institutions can provide additional support to improve academic outcomes and retention rates. The study also discusses practical implications, including the development of pedagogical policies and guidelines based on early predictions, which can help educational institutions maintain strong academic outcomes and enhance their reputation for academic excellence. Future research aims to investigate the impact of individual activities on student performance and explore day-to-day student behaviors, enabling the creation of tailored pedagogical policies and guidelines.
Placement is the process of connecting the selected candidate with the employer. Every student might have a dream of having a job offer when he or she is about to complete her course. All educational institutions aim at having their students well placed in good organizations. The reputation of any institution depends on the placement of its students. Hence, many institutions try hard to have a good placement cell. Classification using machine learning may be utilized to retrieve data from the student-databases. A prediction model that can foretell the eligibility of the students based on their academic and extracurricular achievements is proposed. Related data was collected from many institutions for which the placement-prediction is made. This paradigm is being weighed up with the existing algorithms, and findings have been made regarding the accuracy of predictions. It was found that the proposed algorithm performed significantly better and yielded good results.
The academic performance (scores/credentials) of a student during an educational program is
generally linked to his/her employability (including the placement package he/she receives)
(Cheong et al., 2018). However, in a program like MBA non-academic performance like extracurricular
activities, interpersonal skills, etc., of a student is equally or more important as it could
fetch him/her a better job with a higher package. Moreover, MBA graduates are expected to have
not only academic knowledge but also practical skills such as leadership, attitude, efforts, and
communication. This study relates both academic and non-academic performances of students to
the respective packages they get and proposes a fuzzy expert system (FES) for the evaluation of
students’ performances and its combined effect on placement (outcome). A suitable rule-based
fuzzy inference mechanism (FIM) has been formed and discussed. The principles behind fuzzy
logic in evaluating student academic as well as non-academic performances have been discussed
and how these principles could be applied in measuring its impact on the placement packages
(historical) is illustrated. Results (achieved placement performance/package of students) were
also compared with the existing (traditional) statistical method. The fuzzy logic system was
assessed as a more practical method of evaluation than the traditional method. This research uses
a sample of 56 students in the MBA program who graduated in 2021 from the management
department (b-school) of a premier private university in India. The research found that an increase
in non-academic performance had a greater impact on placement outcomes than an increase
in academic performance alone.
Quality of placements is an important parameter considered by accreditation bodies while ranking Universities or Institutes of Higher Education. Thanks to the increasing automation of placement grooming related activities, universities have started exploring using intelligent computing techniques to anticipate placement status of upcoming batches and identifying factors that play an important role in improving placement quality. In this work, machine learning techniques have been used to predict placement students of engineering students of computer science discipline. Logistic Regression and Decision Tree algorithms gave best prediction performance. Marks in ‘Xth’, ‘XIIth’, ‘Current CGPA’ and ‘Present Attendance’ were among the important factors affecting placement status of students.
Indirectly, student admissions in any particular educational institution depends on student placement, that is why placement of scholars from their learning institutions plays a vital key role which is responsible for building students future. Overall, maximum placement from institutions may define and maintain not only good ranking in Universities but also national or international ranking of institutions. So, institutions strive to improvise and obtain better results from their placement department. This will also be helpful for students to get surety about their placement point of view. By considering various attributes based on which candidate gets hired, from used dataset like exam percentage, work experience, aptitude skills, communication and many more. Using machine learning techniques, the main aim and objective of this study is to predict whether the student gets placed or not. From the aforementioned, by collecting dataset with appropriate attributes to make predictions, we have obtained maximum model accuracy after analysis over Logistic regression 83%, 78% for Random Forest and for Decision tree is 73% using machine learning algorithms. On the basis of obtained/predicted results, institutions can come to know about candidate profiles and also may decide to provide more training if they are lagging somewhere. With this, the model also helps education institutions to maintain their placement percentages for placement departments.KeywordsMachine learningLogistic regressionDecision treeRandom forestPython
Placements include limitless importance for college students and academic organizations. It helps a student to assemble a robust basis for the expert profession beforehand in addition to a virtuous placement file affords an aggressive side to a school or college inside the schooling arcade. Machine learning is a way of statistical evaluation that automates analytical version construction. This paper makes a specialty of a machine that forecasts if a pupil might be located or now no longer primarily based totally on the pupil’s qualifications, ancient statistics, and experience. This forecaster makes use of three machine learning algorithms, namely, Decision Tree, Naïve Bayes, and Random Forest to expect pupil’s placement after which evaluation of those algorithms are performed on the idea of accuracy achieved.
Students aspiring for technical education generally select educational institutions with good track record in campus placements. Many a times the reputation of such institute is determined by the pay packages offered by recruiters to its students. In this context it is pertinent to investigate and identify those factors that may influence the student campus placement chances in technical education. The State of Andhra Pradesh which has a high concentration of technical education institutes was chosen as the study area. A careful review of literature lead to the identification of six hypothetical determinants of student campus placement in technical education. A random sample 250 MBA student’s placement data were gathered from different institutes and six predictor binary logistic regression model was fitted to the data to estimate the odds for the student campus placement. Estimated Results of the study indicate that the chances of campus placement is influenced by four predictors: CGPA, Specialization in PG, Specialization in UG and Gender.
Multi-label classification has attracted increasing attention in various applications, such as medical diagnosis and semantic annotation. With such trend, a large number of ensemble approaches have been proposed for multi-label classification tasks. Most of these approaches construct the ensemble members by using bagging schemes, butfew stacked ensemble approaches are developed. Existing research on stacked ensemble approaches remains active, but several issues remain such as (1) little has been done to learn the weights of classifiers for classifier selection; (2) the relationship between pairwise label correlations and multi-label classification performance has not been investigated sufficiently. To address these issues, we propose a novel stacked ensemble approach that simultaneously exploits label correlations and the process of learning weights of ensemble members. In our approach, first, a weighted stacked ensemble with sparsity regularization is developed to facilitate classifier selection and ensemble members construction for multi-label classification. Second, in order to improve the classification performance, the pairwise label correlations are further considered for determining weights of these ensemble members. Finally, we develop an optimization algorithm based on both of the accelerated proximal gradient and the block coordinate descent techniques to achieve the optimal ensemble solution efficiently. Extensive experiments on publicly available datasets and real Cardiovascular and Cerebrovascular Disease datasets demonstrate that our proposed algorithm outperforms related state-of-the-art methods from perspectives of benchmarking and real-world applications.
Background:
Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.
Methods:
In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction.
Results:
We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered.
Conclusion:
This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.
ABSTRACT
Purpose.
Data mining is the forthcoming research area to solve different problems
and classification is one of main problem in the field of data mining. In this paper, we use
two classification algorithms J48 and Sequential Minimal Optimization alias SMO of the
Weka interface.
Methodology.
It can be used for testing several datasets. The performance of J48 and
Sequential Minimal Optimization has been analyzed to choose the better algorithm based
on the conditions of the datasets. The datasets have been chosen from UCI Machine Learn-
ing Repository.
Findings.
Algorithm J48 is based on C4.5 decision-based learning and algorithm
Sequential Minimal Optimization uses the Support Vector Machine approach for classifica-
tion of datasets. When comparing the performance of both algorithms we found Sequential
Minimal Optimization is better algorithm in most of the cases.
Originality.
This is the first implemented research work up to my knowledge, data
sets classification problem handled in data mining using SMO with Weka interface.
(PDF) ANALYSIS OF CLASSIFICATION ALGORITHMS ON DIFFERENT DATASETS. Available from: https://www.researchgate.net/publication/330611425_ANALYSIS_OF_CLASSIFICATION_ALGORITHMS_ON_DIFFERENT_DATASETS [accessed Nov 04 2019].
In some situations, it is necessary to measure personal programming skills. For example, often students must be divided according to skill level and motivation to learn or companies recruiting employees must rank candidates by evaluating programming skills through programming tests, programming contests, etc. This process is burdensome because teachers and recruiters must prepare, implement, and evaluate a placement examination. This paper tries to predict the placement and ranking results of programming contests via machine learning without such an examination. Explanatory variables used for machine learning are classified into three categories: Psychological Scales, Programming Tasks, and Student-answered Questionnaires. The participants are university students enrolled in a Java programming class. One target variable is the placement result based on an examination by a teacher of a class and the ranking results of the programming contest. Our best classification model with a decision tree has an F-measure of 0.912, while our best ranking model with an SVM-rank has an nDCG of 0.962. In both prediction models, the best explanatory variable is from the Programming Task followed in order by Psychological Sale and Student-answered Questionnaire. Our classification model uses 9 explanatory variables, while our ranking model uses 20 explanatory variables. These include all three types of explanatory variables. The source code complexity, which is a source code metrics from Programming Task, shows best performance when the prediction uses only one explanatory variable. Contribution (1), this method can automate some of the teacher’s workload, which may improve educational quality and increase the number of acceptable students in the course. Contribution (2), this paper shows the potential of using difficult-to-formulate information for an evaluation such as a Psychological Scale is demonstrated. These are the contributions and implications of this paper.
This research investigates the problem of news articles classification.
The classification is performed using N-gram textual features extracted from
text and visual features generated from one representative image. The application
domain is news articles written in English that belong to four categories:
Business-Finance, Lifestyle-Leisure, Science-Technology and Sports downloaded
from three well-known news web-sites (BBC, Reuters, and TheGuardian).
Various classification experiments have been performed with the
Random Forests machine learning method using N-gram textual features and
visual features from a representative image. Using the N-gram textual features
alone led to much better accuracy results (84.4%) than using the visual features
alone (53%). However, the use of both N-gram textual features and visual features
led to slightly better accuracy results (86.2%). The main contribution of
this work is the introduction of a news article classification framework based on
Random Forests and multimodal features (textual and visual), as well as the late
fusion strategy that makes use of Random Forests operational capabilities.
This qualitative study investigates the career placement concerns of international graduate students returning to their home countries, heading to other countries, or remaining in the United States after their education. Using a phenomenological framework, structured interviews were conducted with 24 participants (i.e., 18 international students, 1 naturalized status student, 3 faculty members, and 2 career counselors). Assertions that emerged suggested that the students possess diverse career plans influenced by numerous unique factors. Major, gender, and geography were not salient factors affecting the students' career placement needs. Students used contacts in their academic fields far more than they used campus career services, due to negative perceptions of or limited services that the center could provide. Based on these findings, more outreach to the international student community and collaboration between the career counseling and placement center and other campus departments are recommended.
In this chapter, we give an introduction to classification algorithms and the metrics that are used to quantify and visualize their performance. We first briefly explain what we mean with a classification algorithm, and, as an example, we describe in more detail the naive Bayesian classification algorithm. Using the concept of a confusion matrix, we next define the various performance metrics that can be derived from it, including sensitivity and specificity that define the two dimensions of ROC space. We next argue that correctly evaluating the performance of a classification algorithm requires taking into account the conditions in which the algorithm has to operate in practice. These so-called operating conditions consist of two elements: class skew and cost skew. We show that both elements can be combined into a single parameter that defines cost, and that iso-cost curves are straight lines in ROC space.
This paper is written to support the real time prediction of the age group of the students towards four different ICT parameters such as the development and availability of modern ICT Resources (DAICT), Attitude of students towards ICT and mobile technology in education (AICTM), Use of ICT and mobile technology in education (UICTM) and educational benefits of ICT and mobile technology in education (EBICTM) in Hungarian and Indian University. The investigator has collected the primary dataset during the academic year 2017-18 from two countries. The present study formulates the idea of feature aggregation significantly improved the prediction accuracy of the age group of students towards ICT parameters. The authors solved a multiclassification problem using tested primary dataset with 5 popular supervised machine learning classifiers such as K-nearest neighbor (KNN), Random forest (RF) and Support vector machine (SVM), Bayesian network (BN) and decision tree (C5.1) in SPSS IBM Modeler version 18.1. The concept of aggregation of features provides maximum accuracy of 91.4% is provided by joint classifiers (RT, SVM, and BN) in the prediction of age against 37 features. The second winner group (RT, SVM, C5.1) provides 79.2% accuracy only with 16 features. Although, aggregation of features has significantly increased the accuracy in prediction of student's age group towards four ICT parameters instead of an individual. Findings of the study also stabilized dataset with 337 instances with 37 features in the prediction of the student's age group belongs to India and Hungary. This paper may be play a significant role in development and support of real time age prediction of the student's age at individual and collective parameters of ICT in both countries.
Data Mining is the process of extracting useful information from large sets of data. Data mining enablesthe users to have insights into the data and make useful decisions out of the knowledge mined from databases. The purpose of higher education organizations is to offer superior opportunities to its students. As with data mining, now-a-days Education Data Mining (EDM) also is considered as a powerful tool in the field of education. It portrays an effective method for mining the student’s performance based on various parameters to predict and analyze whether a student (he/she) will be recruited or not in the campus placement. Predictions are made using the machine learning algorithms J48, Naïve Bayes, Random Forest, and Random Tree in weka tool and Multiple Linear Regression, binomial logistic regression, Recursive Partitioning and Regression Tree (rpart), conditional inference tree (ctree) and Neural Network (nnet) algorithms in R studio. The results obtained from each approaches are then compared with respect to their performance and accuracy levels by graphical analysis. Based on the result, higher education organizations can offer superior training to its students.
Machine learning is the process which converts the information into intelligent actions. This paper presents a literature review on application of different Machine Learning algorithms on huge amount of data collected by the academic institutes. Predictive analytics using the machine learning algorithms has become a new tool of this modern era, as it assists academic institutions in improving the retention and success rate of students and to get overview of performance before the examination to reduce the risk of failure. The main aim of the paper is to describe the various ways in which the machine learning is used in educational institutes and how institutes can get prediction of students' performance and the important features that are needed to be considered while making prediction for different things. In addition to this the study also compares the prediction given by different machine learning algorithms. Lastly the paper concludes that the prediction of the students' performance can be made more precise and accurate by considering the learning style of students, their motivation and interest, concentration level, family background, personality type, information processing ability and the way they attempt the exams.
The psychological state of the student has deep influence on their academic performance is being proved by various studies. The paper demonstrates the impact of student's psychology and their learning and study skills in the predicting their academic performance. The experiment was performed on the real time data collected from final year students. The matriculate and pre-university examination scores, five semester scores along with data on the motivation level, information processing ability and other learning and study skills were taken as input to the model to predict the Cumulative Grade Point Average(CGPA) of the sixth semester. Two machine learning algorithms were used to test the impact of students' psychology on prediction which includes Neural Network for numeric prediction of sixth semester CGPA and Decision Tree for classification of failures in sixth semester. The performance of the models were evaluated using the coefficient of correlation R and the Mean Squared Error. The accuracy of the prediction increases about 4 to 6%. The study reveals that level of motivation in student's life and the way they perceive the information and use the available study materials for the examination, all counts in prediction of their examination performance.
The support vector machine is a new type of machine learning methods based on statistical learning theory. Because of good promotion and a higher accuracy, support vector machine has become the research focus of the machine learning community. This paper introduces the basic theory of support vector machine, the basic idea of the classification and currently used support vector machine classification algorithm. Practical problems with which an algorithm, and proves the effectiveness of the algorithm, the final outlook of the prospects of support vector machines in classification applications. Finally the prospect of the prospect of support vector machines in classification applications.
Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on engineering students' past performance data to generate the model and this model can be used to predict the students' performance. It will enable to identify the students in advance who are likely to fail and allow the teacher to provide appropriate inputs.
In this paper we present a perspective on the overall process of developing classifiers for real-world classification problems. Specifically, we identify, categorize and discuss the various problem-specific factors that influence the development process. Illustrative examples are provided to demonstrate the iterative nature of the process of applying classification algorithms in practice. In addition, we present a case study of a large scale classification application using the process framework described, providing an end-to-end example of the iterative nature of the application process. The paper concludes that the process of developing classification applications for operational use involves many factors not normally considered in the typical discussion of classification models and algorithms.
This paper analyzes how to introduce machine learning algorithms into the process of direct volume rendering. A conceptual
framework for the optical property function elicitation process is proposed and particularized for the use of attribute-value
classifiers. The process is evaluated in terms of accuracy and speed using four different off-the-shelf classifiers (J48,
Naïve Bayes, Simple Logistic and ECOC-Adaboost). The empirical results confirm the classification of biomedical datasets as
a tough problem where an opportunity for further research emerges.
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