Guoyin Wang

Guoyin Wang
Chongqing University of Posts and Telecommunications · College of Computer Science and Technology

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104
Publications
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Publications

Publications (104)
Article
Full-text available
Learning label noise is gaining increasing attention from a variety of disciplines, particularly in supervised machine learning for classification tasks. The k nearest neighbors (kNN) classifier is often used as a natural way to edit the training sets due to its sensitivity to label noise. However, the kNN-based editor may remove too many instances...
Article
Aspect sentiment triplet extraction (ASTE) is a popular subtask related to aspect-based sentiment analysis (ABSA). It extracts aspects and their associated opinion expressions and sentiment polarities from comment sentences. Previous studies have proposed a multitask learning framework that jointly extracts aspect and opinion terms and treats the s...
Article
Full-text available
Nowadays, attributed multiplex heterogeneous network (AMHN) representation learning has shown superiority in many network analysis tasks due to its ability to preserve both the structure of the network and the semantics of the nodes. However, few people consider the correlation between content attributes within each node. No personalized analysis m...
Article
The interpretability of convolutional neural networks (CNNs) is attracting increasing attention. Class activation maps (CAM) intuitively explain the classification mechanisms of CNNs by highlighting important areas. However, as coarse-grained explanations, classical CAM methods are incapable of explaining the classification mechanism in detail. Ins...
Article
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a specific photo from a given query sketch. However, its widespread applicability is limited because it is difficult for most people to draw a complete sketch, and the drawing process is often time consuming. In this study, we aim to retrieve the target photo fr...
Article
Full-text available
In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, these deep neural models are still considered as “black box” for most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these fe...
Article
As the significant expansion of classical rough sets, local rough sets(LRS) is an effective model for processing large-scale datasets with finite labels. However, the process of establishing a category of monotonic uncertainty measure with strong distinguishing ability for LRS remains ambiguous. To construct this model, both the monotonicity of loc...
Preprint
Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene. In conventional models, the edge image generated is ambiguous, and the edge lines are also very thick, which typically necessitates the use of non-maximum suppression (NMS) and morphological...
Article
Full-text available
Soft clustering can be regarded as a cognitive computing method that seeks to deal with the clustering with fuzzy boundary. As a classical soft clustering algorithm, rough k-means (RKM) has yielded various extensions. However, some challenges remain in existing RKM extensions. On the one hand, the user-defined cutoff threshold is subjective and can...
Article
Full-text available
Mapping the vertices of network onto a tree helps to reveal the hierarchical community structures. The leading tree is a granular computing (GrC) model for efficient hierarchical clustering and it requires two elements: the distance between granules, and the density calculated in Euclidean space. For the non-Euclidean network data, the vertices nee...
Preprint
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo in a given query sketch. However, its widespread applicability is limited by the fact that it is difficult to draw a complete sketch for most people, and the drawing process often takes time. In this study, we aim to retrieve the target photo...
Preprint
Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve the efficiency of granular-ball...
Preprint
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In...
Article
A multi-granularity knowledge space is a computational model that simulates human thinking and solves complex problems. However, as the amount of data increases, the multi-granularity knowledge space will have a larger number of layers, which will reduce its problem-solving ability. Therefore, we define a knowledge space distance measurement and pr...
Article
We present a model, called relative probability density (RPD), to detect label noise by utilizing the contrasting characteristics in different classes. RPD has a natural ratio structure so that a powerful measurement, the Kullback–Leibler Importance Estimation Procedure (KLIEP), can be directly applied for its calculation instead of calculating the...
Preprint
In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, such deep neural models are still regarded as black box in most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how they are processed by CNNs....
Article
In recent years, convolutional neural networks (CNNs) have been successfully applied in the field of image processing, and have been deployed to a variety of artificial intelligence systems. However, such neural models are still considered to be “black box” for most tasks. Two of fundamental issues underlying this problem are as follows: 1. What ty...
Article
Full-text available
This article presents a general sampling method, called granular-ball sampling (GBS), for classification problems by introducing the idea of granular computing. The GBS method uses some adaptively generated hyperballs to cover the data space, and the points on the hyperballs constitute the sampled data. GBS is the first sampling method that not onl...
Article
Naive Bayes classifier (NBC) is a classical binary generative classifier that has been extensively researched and developed for use in various applications owing to its simplicity and high efficiency. However, in practice, the distinct advantages of the NBC are often challenged by the conditional independence assumption among attributes and the zer...
Article
The Synthetic Minority Oversampling Technique (SMOTE) is a prevalent method for imbalanced classification. The plain SMOTE is intrinsically flawed in that it generates new samples blindly, thus being susceptible to label noise. Many variants of the SMOTE focus on balancing the number of classes and avoiding the introduction or removal of label-nois...
Article
This paper presents an efficient graph semisupervised learning (GSSL) method that meets the criterion of optimization without iterations. Most existing GSSL methods require iterative optimization to achieve a preeer relationships. Additionally, existing GSSL methods must learn from scratch for unseen data because graph structures are specifically b...
Article
Recommender systems are an effective tool to resolve information overload by enabling the selection of the subsets of items from a universal set based on user preferences. The operation of most of recommender systems depends on the prediction ratings, which may introduce a degree of uncertainty into the process of recommendation. However, systems e...
Article
Full-text available
This article presents a simple sampling method, which is very easy to be implemented, for classification by introducing the idea of random space division, called ``random space division sampling'' (RSDS). It can extract the boundary points as the sampled result by efficiently distinguishing the label noise points, inner points, and boundary points....
Article
Full-text available
Multi-scale decision system (MDS) is an effective tool to describe hierarchical data in machine learning. Optimal scale combination (OSC) selection and attribute reduction are two key issues related to knowledge discovery in MDSs. However, searching for all OSCs may result in a combinatorial explosion, and the existing approaches typically incur ex...
Article
Full-text available
Mitigating label noise is a crucial problem in classification. Noise filtering is an effective method of dealing with label noise which does not need to estimate the noise rate or rely on any loss function. However, most filtering methods focus mainly on binary classification, leaving the more difficult counterpart problem of multiclass classificat...
Article
Imbalanced classification is an important task in supervised learning, and Synthetic Minority Over-sampling Technique (SMOTE) is the most common method to address it. However, the performance of SMOTE deteriorates in the presence of label noise. Current generalizations of SMOTE try to tackle this problem by either selecting some samples in minority...
Article
Granular computing is an efficient and scalable computing method. Most of the existing granular computing-based classifiers treat the granules as a preliminary feature procession method, without revising the mathematical model and improving the main performance of the classifiers themselves. So far, only few methods, such as the G-svm and WLMSVM, h...
Article
Full-text available
Online social networks play more and more important roles in the modern society in terms of the rapid and large scale information spread. Many efforts have been made to understand these phenomena in the computer science communities and other relative fields, ranging from popular topic detection to information diffusion modeling. In this article, a...
Article
Full-text available
The existing noise detection methods required the classifiers or distance measurements or data overall distribution, and ‘curse of dimensionality’ and other restrictions made them insufficiently effective in complex data, e.g. different attribute weights, high-dimensionality, containing feature noise, nonlinearity, etc. This is also the main reason...
Article
Full-text available
Graph based semi-supervised learning (GSSL) has intuitive representation and can be improved by exploiting the matrix calculation. However, it has to perform iterative optimization to achieve a preset objective, which usually leads to low efficiency. Another inconvenience lying in GSSL is that when new data come, the graph construction and the opti...
Article
Full-text available
Constructing information granules (IGs) has been of significant interest to the discipline of granular computing. The principle of justifiable granularity has been proposed to guide the design of IGs, opening an avenue of pursuits of building IGs carried out on a basis of well-defined and intuitively appealing principles. However, how to improve th...
Article
Detecting clusters of arbitrary shape and constantly delivering the results for newly arrived items are two critical challenges in the study of data stream clustering. However, the existing clustering methods could not deal with these two problems simultaneously. In this paper, we employ the density peaks based clustering (DPClust) algorithm to con...
Article
Full-text available
Approximation computation is a critical step in rough sets theory used in knowledge discovery and other related tasks. In practical applications, an information system often evolves over time by the variation of attributes or objects. Effectively computing approximations is vital in data mining. Dominance-based rough set approach can handle informa...
Article
Confidential dominance relation based rough set is a model of incomplete ordered information processing, computation of approximations of which is a core issue. In real-life applications, the attribute set is dynamically changed. According to the variation of the attribute set, confidential dominance and dominated class are firstly calculated. Then...
Conference Paper
A theory of three-way decisions is formulated based on the notions of three regions and associated actions for processing the three regions. Three-way decisions play a key role in everyday decision-making and have been widely used in many fields and disciplines. A group of Chinese researchers further investigated the theory of three-way decision an...
Article
Incomplete ordered information processing is a common problem in the real life. Various extended dominance relation rough set models are already proposed to solve the incomplete ordinal decision problem. But there exits some ambivalence over the real semantics because the characteristics of the order relation are not considered. Therefore, the conf...
Article
Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields. Determining the number of clusters in a data set is one of the most challenging and difficult problems in cluster analysis. To combat the problem, this paper proposes...
Article
The representation and processing of uncertainty information is one of the key basic issues of the intelligent information processing in the face of growing vast information, especially in the era of network. There have been many theories, such as probability statistics, evidence theory, fuzzy set, rough set, cloud model, etc., to deal with uncerta...
Conference Paper
Uncertainty is one basic feature in the information processing, and the expressing and processing of uncertain information have attracted more attentions. There are many theories introduced to process the uncertain information, such as probability theory, random set, evidence theory, fuzzy set theory, rough set theory, cloud model theory and so on....
Article
Granular computing is one of the important methods for extracting knowledge from data and has got great achievements. However, it is still a puzzle for granular computing researchers to imitate the human cognition process of choosing reasonable granularities automatically for dealing with difficult problems. In this paper, a Gaussian cloud transfor...
Conference Paper
The expressing and processing of uncertain concepts is a fundamental problem in artificial intelligence. Several theoretical models have been proposed for solving this problem, such as probability theory, fuzzy sets, rough sets, cloud model, et al. Unfortunately, human deals with uncertain concepts based on words (concept intension), while computer...
Article
Full-text available
Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an...
Chapter
Cognitive Computing (CC) is an emerging paradigm of intelligent computing theories and technologies based on cognitive informatics, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. The development of Cognitive Computers (cC) is centric in cognitive computing methodologies. A...
Conference Paper
Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields. A fundamental and difficult problem in cluster analysis is how many clusters are appropriate for the description of a given system. The objective of this paper is to d...
Conference Paper
Uncertainty exists almost everywhere. In the past decades, many studies about randomness and fuzziness were developed. Many theories and models for expressing and processing uncertain knowledge, such as probability & statistics, fuzzy set, rough set, interval analysis, cloud model, grey system, set pair analysis, extenics, etc., have been proposed....
Article
Attribute reduction is an important process in rough set theory. More minimal attribute reductions are expected to help clients make decisions in some cases, though the minimal attribute reduction problem (MARP) is proved to be an NP-hard problem. In this paper, we propose a new heuristic approach for solving the MARP based on the ant colony optimi...
Conference Paper
Data processing and knowledge discovery for massive data is always a hot topic in data mining, along with the era of cloud computing is coming, data mining for massive data is becoming a highlight research topic. In this paper, attribute reduction for massive data based on rough set theory is studied. The parallel programming mode of MapReduce is...
Conference Paper
Similarity measure between trajectories is considered as a pre-processing procedure of trajectory data mining. A lot of shaped-based and time-based methods on trajectory similarity measure have been proposed by researchers recently. However, these methods can not perform very well on constrained trajectories in road network because of the inappropr...
Article
Full-text available
Cognitive Computing CC is an emerging paradigm of intelligent computing theories and technologies based on cognitive informatics, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. The development of Cognitive Computers cC is centric in cognitive computing methodologies. A cC...
Conference Paper
Expression recognition is popular research focus in Artificial Intelligence and Pattern Recognition. Feature fusion is one of the most important technical methods in expression recognition. To study how the feature information extracted from different part of the face play the role in facial expression recognition, experiments have been done and sh...
Conference Paper
Emotion recognition is very important for applications of human-computer intelligent interaction. It is always performed on facial or audio information with such method as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning is a hot topic in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel...
Conference Paper
Cognitive Computing (CC) is an emerging paradigm of intelligent computing theories and technologies based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. The development of Cognitive Computers (cC) is centric in cognitive computing methodologies. A c...
Conference Paper
Full-text available
Particle swarm optimization (PSO) has been shown to perform well on many optimization problems. However, the PSO algorithm often can not find the global optimum, even for unimodal functions. It is necessary to study the local search ability of PSO. The interval compression method and the probabilistic characteristic of the searching interval of par...
Conference Paper
We are living in an information technology (IT) era now. Advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data and information. What will be the next step of IT? Many researchers predict that the next step of IT might be...
Conference Paper
Full-text available
Social component and cognitive component are important for updating particles’ velocity. In classical particle swarm optimization, the social component and the cognitive component in the updating velocity equation are supposed to be independent. It is reasonable to consider that the dependence between objects reflects the underlying mechanisms. Thi...
Conference Paper
Full-text available
Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper summa...
Chapter
Human-computer intelligent interaction (HCII) is becoming more and more important in daily life, and emotion recognition is one of the important issues of HCII. In this paper, a novel emotion recognition method based on dynamic ensemble feature selection is proposed. Firstly, a feature selection algorithm is proposed based on rough set and domain-o...
Article
Human beings are born with a natural capacity of recovering shape from merely one image. However, it is still a challenging mission for current techniques to make a computer have such an ability. To simulate the modeling procedure of human visual system, a Ternary Deformation Framework (TDF) is proposed to reconstruct a realistic 3D face from one 2...
Article
Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerg...
Conference Paper
Attribute reduction is an important process in rough set theory. More minimal attribute reductions are expected to help clients make decisions in some cases, though the minimal attribute reduction problem (MARP) is proved to be an NP-hard problem. In this paper, we propose a new heuristic approach for solving the MARP based on the ant colony optimi...
Conference Paper
Affective computing is becoming a more and more important topic in intelligent computing technology. Emotion recognition is one of the most important topics in affective computing. It is always performed on face and voice information with such technology as ANN, fuzzy set, SVM, HMM, etc. In this paper, based on the idea of data driven data mining a...
Article
In this paper, the fuzzy quotient space theory for the cut-relation of fuzzy equivalence relation with any threshold is discussed. A method is proposed for hierarchically constructing normalized isosceles distance function between different quotient spaces. And a hierarchical structure of fuzzy quotient space is constructed. The relation between th...
Conference Paper
Full-text available
Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performin...
Conference Paper
This paper presents a novel method to reconstruct realistic 3D faces from a set of control points: (i) a local deformation approach is proposed, which could effectively preserve the information when only a few features are known; (ii) a Ternary Deformation Framework (TDF), combining the strengths of both local modification and global calculation, i...
Article
The denotational and expressive needs in cognitive informatics, computational intelligence, software engineering, and knowledge engineering lead to the development of new forms of mathematics collectively known as denotational mathematics. Denotational mathematics is a category of mathematical structures that formalize rigorous expressions and long...
Article
This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description of the following things: Chinese research groups on rough set with their URLs for web pages, names of principal researchers (supervisors), nu...
Conference Paper
A novel method, called dynamic component deforming model, is proposed to reconstruct the face shape from a 2D image based on feature points. Assuming that human face belongs to a linear class, principal components learned from a 3D face database are used in order to constrain the results. Different from the fixed components used in the traditional...
Conference Paper
Face can be modeled with a good approximation by a few patches covering some significant regions. Therefore, in many face-related applications, it is critical that face is segmented into patches before being analyzed. This paper presents an automatic 3D face image segmentation algorithm based on feature extraction. Given a 3D face, the 2D texture i...