Jorge Casillas

• PhD
• Professor (Associate) at University of Granada

Fairness constitutes a concern within machine learning (ML) applications. Currently, there is no study on how disparities in classification complexity between privileged and unprivileged groups could influence the fairness of solutions, which serves as a preliminary indicator of potential unfairness. In this work, we investigate this gap, specifica...

Current fairness metrics and mitigation techniques provide tools for practitioners to asses how non-discriminatory Automatic Decision Making (ADM) systems are. What if I, as an individual facing a decision taken by an ADM system, would like to know: Am I being treated fairly? We explore how to create the affordance for users to be able to ask this...

Fairness in artificial intelligence has emerged as a critical ethical concern, with most research focusing on classification tasks despite the prevalence of regression problems in real-world applications. We address this gap by presenting a general procedure for measuring fairness in regression problems, focusing on statistical parity as a fairness...

College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work;...

There exists a perception, which is occasionally incorrect, that the presence of machines in decision-making processes leads to improved outcomes. The rationale for this belief is that machines are more trustworthy since they are not prone to errors and possess superior knowledge to deduce what is optimal. Nonetheless, machines are crafted by human...

La pandemia por COVID-19 ha tenido un gran impacto en todo el mundo. Afecta, no solo la salud pública sino también la economía y la educación. Este estudio se enfoca en examinar la relación entre los distintos factores del profesorado para atender el aprendizaje del estudiantado bajo situación de pandemia. Se vale de las oportunidades, las habilida...

La promoción de una educación de calidad en las instituciones de enseñanza superior promueve la autoeficacia. La utilidad del trabajo se ha dirigido al análisis de las características del profesorado y el éxito académico de los estudiantes al final del primer año en el contexto universitario. La población estudiada fue de 6690 estudiantes y 256 pro...

The technological era in which we live has supposed an exponential rise in the quantity of data daily-generated in the Internet. Social networks and particularly Twitter has been one of the most disruptive factors in this era, allowing people to share easily opinions and ideas. Data generated in this social network is an example of streams, which a...

Fairness is an increasingly important topic in the world of Artificial Intelligence. Machine learning techniques are widely used nowadays to solve huge amounts of problems, but those techniques may be biased against certain social groups due to different reasons. Using fair classification methods we can attenuate this discrimination source. Neverth...

Fair machine learning has been focusing on the development of equitable algorithms that address discrimination. Yet, many of these fairness‐aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. In this study, a novel methodology is presented t...

Fair machine learning works have been focusing on the development of equitable algorithms that address discrimination of certain groups. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. We present the first met...

Institutions of higher education omit to a certain extent the factors that delay the rates of promotion of university students. The delay cannot always be disclosed due to the diversity of study programs, from the beginning of the career to the completion of the program and graduation. This paper used the student data set for 5 full academic years...

Building energy modelling presents a good tool for estimating building energy consumption. Different modelling approaches exist in literature comprising white-box/physical/calculation-based models, black-box/statistical/measurement-based models or hybrid models combining the former two. Our work presented in this paper deals with a calculation-base...

This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer’s disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interes...

Nowadays society is deeply affected by web content. A web site, regardless of its category, can provide or not for users their needs. To identify its strengths and weaknesses, a process of analyzing and assessing its quality, via some criteria, is necessary. Assessing web sites is considered as a Multiple Criteria Decision Making problem (MCDM), wi...

The amount of data being generated in industrial and scientific applications is constantly increasing. These are often generated as a chronologically ordered unlabeled data flow which exceeds usual storage and processing capacities. Association stream mining is an appealing field which models complex environments online by finding relationships amo...

The widely accepted importance of energy efficiency in the building sector is continuously acknowledged by the engineering and research community, as proven by the quantity and diversification of relevant modeling proposals in literature. It is often difficult to collect and assess this plethora of approaches and sometimes the diversity of the feat...

Due to the variability of variables, the estimation of effort in software development is considered as a very difficult task. Maybe due to small databases, uncertain and very subjective information. For these reason, in this paper, we introduce a study of the effect of variables in the software development effort estimation process. The estimation...

The development effort estimation is one of the most difficult problems in software project management. It is one of the most critical aspects in the early stages of the software project. Several software development effort estimation models have been proposed, however, these models are not able to obtain more than a 25 percent of accuracy, neither...

Multiple Criteria Decision Making (MCDM) is a widely used discipline in everyday life especially to make decisions about conflict and multiple criteria that need to be evaluated and analyzed. In this paper, the aim is to explore the known MCDM techniques to assess web sites information in specific domains or identify the current developments in on-...

Knowledge Discovery in Databases (KDD) is a recent research field belonging to artificial intelligence whose main aim is the identification of new, potentially useful, and understandable patterns in data. Keywords: knowledge discovery; artificial intelligence; data mining; business data; machine learning

Marketing intelligent systems (MkiS) are artificial intelligence-based systems applied to aid decision-making in marketing management, and thus belong to the broad category of knowledge-driven systems.

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to...

The study here highlights the potential that analytical methods based on Knowledge Discovery in Databases (KDD) methodologies have to aid both the resolution of unstructured marketing/business problems and the process of scholarly knowledge discovery. The authors present and discuss the application of KDD in these situations prior to the presentati...

Intelligent systems have particular potentialities and strengths to support decisional situations faced by companies, especially those of a strategic nature, where good strategic intelligence is necessary. In this paper, we carry out an historical literature review of artificial intelligence-based systems applied to marketing, covering a time perio...

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of mar...

This symposium was born as a research forum to present and discuss original, rigorous and significant contributions on Artificial Intelligence-based (AI) solutions—with a strong, practical logic and, preferably, with empirical applications—developed to aid the management of organizations in multiple areas, activities, processes and problem-solving;...

The huge amount of spam messages has limited the benefits introduced by e-mail communications. Therefore, spam filters are indispensable to fight against spam deliveries. However, the development of spam filters is very expen- sive whereas the usage of external filtering services can damage communications privacy. In such situation, we introduce an...

MarkiS is a software platform that uses intelligent systems for knowl-edge extraction from large marketing databases. MarkiS allows the marketing expert to model, learn and analyze marketing models using two different genetic algorithms for learning fuzzy systems with multi-item variables. Using these intel-ligent systems the expert can obtain more...

In this research a three staged hybrid genetic-fuzzy systems modeling methodology is developed and applied to an empirical data set in order to determine the hidden fuzzy if-then rules. The empirical data was collected in an earlier study in order to establish the relations among human capital, organizational support and innovativeness. The results...

The 2012 International Symposium on Management Intelligent Systems is believed to be the first international forum to present and discuss original, rigorous and significant contributions on Artificial Intelligence-based (AI) solutions—with a strong, practical logic and, preferably, with empirical applications—developed to aid the management of orga...

Multi-link wheeled robots provide interesting opportunities within many areas such as inspection and maintenance of pipes or vents. A key functionality in order to perform such operations, is that the robot can follow a predefined path fast and accurately. In this paper we present an algorithm to learn the path-following behavior for a set of motio...

The extraction of models from data streams has become a hot topic in data mining due to the proliferation of problems in which data are made available online. This has led to the design of several systems that create data models online. A novel approach to online learning of data streams can be found in Fuzzy-UCS, a young Michigan-style fuzzy-class...

Multi-objective genetic learning of Fuzzy Rule-Based Systems (FRBSs) is a very prolific investigation trend. The use of more optimization objectives to cover more aspects of the fuzzy model is very convenient, but also leads to a many-objective problem that is intractable with classical algorithms. This paper proposes three distinct categories of i...

In view of dynamicity on road networks and the sharp increase of traffic jam states, the road traffic management becomes more complex. It is clear that the shortest path algorithm based only on road length is no longer relevant.We propose in this paper a hybrid method based on two stages based on ant colony behavior and hierarchical fuzzy system. T...

Marketing-oriented firms are especially concerned with modeling consumer behavior in order to improve their information and aid their decision processes on markets. For this purpose, marketing experts use complex models and apply statistical methodologies to infer conclusions from data. In the recent years, the application of machine learning has b...

In view of the high dynamicity of traffic flow and the polynomial increase in the number of vehicles on road networks, the route choice problem becomes more complex. A classical shortest path algorithm based only on road length is no longer relevant. We propose in this paper an adaptive vehicle guidance system instigated from the ants behavior, wel...

Nowadays, automatic learning of fuzzy rule-based systems is being addressed as a multi-objective optimization problem. A new research area of multi-objective genetic fuzzy systems (MOGFS) has capture the attention of the fuzzy community. Despite the good results obtained, most of existent MOGFS are based on a gross usage of the classic multi-object...

a b s t r a c t Service robots will play an increasing and more important role in the society in the next years. One of the main challenges is to endow robots with enough autonomy to operate on real environments. To reach that goal, the design of controllers to solve simple tasks must be automatized. Engineers look for learning algorithms that are...

The success of companies is partly dependent on the generation of suitable knowledge upon which to base decision-making, and due to the centrality of the marketing function in organizations, marketing-related knowledge is of strategic relevance. In this connection, it is unlikely to reach a full understanding of any set of systems supporting market...

In its introduction this paper discusses why marketing professionals do not make satisfactory use of the marketing models posed by academics in their studies. The main body of this research is characterised by the proposal of a brand new and complete methodology for knowledge discovery in databases (KDD), to be applied in marketing causal modelling...

A new algorithm for tuning fuzzy partitions with a high interpretability degree is proposed. The set of input variables, the number of linguistic terms per variable, and the type (triangular or trapezoidal) and parameters of the membership functions is tuned by an efficient process that endows the algorithm with capability to deal with large-scale...

Genetic fuzzy systems (GFS) are based on the use of genetic algorithms for designing fuzzy systems, and for providing them with learning and adaptation capabilities. In this context, fuzzy sets represent linguistic granules of information, contained in the antecedents and consequents of the rules, whereas the data used in the genetic learning is as...

This paper presents Fuzzy-UCS, a Michigan-style learning fuzzy-classifier system specifically designed for supervised learning tasks. Fuzzy-UCS is inspired by UCS, an on-line accuracy-based learning classifier system. Fuzzy-UCS introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining si...

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds ...

Abstract When a flexible fuzzy rule structure such as those with antecedent in conjunctive normal form is used, the interpretability of the obtained fuzzy model is significantly improved. However, some important prob- lems appear related to the interaction among this set of rules. Indeed, it is relatively easy to get inconsistencies, lack of comple...

The main problem currently faced by market-oriented firms is not the availability of information (data), but the possession of appropriate levels of knowledge to take the right decisions. This is common background for firms. In this regard, marketing professionals and scholars highlight the necessity for knowing and explaining consumers’ behaviour...

A methodology for learning behaviors in mobile robotics has been developed. It consists of a technique to automatically generate input–output data plus a genetic fuzzy system that obtains cooperative weighted rules. The advantages of our methodology over other approaches are that the designer has to choose the values of only a few parameters, the o...

This paper presents CSar, a Michigan-style learning classifier system designed to extract quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners is that it evolves the knowledge online and it is thus prepared to adapt its knowledge to changes in the variable a...

A new algorithm is proposed to learn fuzzy partitions with a high interpretability degree. The set of input variables, the number of linguistic terms per variable, and the type (triangular or trapezoidal) and parameters of the membership functions are learnt by means of a meta-algorithm that uses a simple learning method to generate a fuzzy rule se...

When we face a problem with a high number of vari- ables by a standard fuzzy system, the number of rules increases expo- nentially and then the obtained fuzzy system is scarcely interpretable. This problem can be handled by arranging the inputs in hierarchical ways. The paper presents a multi-objective Genetic Algorithm that learns Serial Hierarchi...

This paper shows part of a larger interdisciplinary research focused on developing artificial intelligence-based analytical tools to aid the marketing managers' decisions on consumer markets. In particular, here it is presented and tested a knowledge discovery methodology based on genetic-fuzzy systems - a Soft Computing (SC) method that jointly ma...

Marketing-oriented firms are especially concerned with modeling con- sumer behavior to improve their information and aid their decision processes on markets. For this purpose, marketing experts use complex models and apply statistical methodologies to infer conclusions from data. Recently, the application of machine learning has been detected as a...

Abstract During the last decade, research on Genetic- Based Machine Learning has resulted in several proposals of supervised learning methodologies,that use evolutionary algorithms,to evolve,rule-based classification models. Usually, these new GBML approaches are accompanied by little experimentation,and there is a lack of comparisons among differe...

This paper introduces an approximate fuzzy representation to Fuzzy- UCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies whether the flexibility provided by the approximate representation results in a significant improvement of the accuracy of the mod- els evolved by the system. We test Fuzzy-UCS w...

System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mai...

This paper presents CSar, a Michigan-style Learning Clas- sier System which has been designed for extracting quanti- tative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing asso- ciation rule miners is that it evolves the knowledge on-line and so it is prepared to adapt its knowledge to cha...

This chapter gives insight in the use of Genetic-Based Machine Learning (GBML) for supervised tasks. Five GBML systems which represent different learning methodologies and knowledge representations in the GBML paradigm are selected for the analysis: UCS, GAssist, SLAVE, Fuzzy AdaBoost, and Fuzzy LogitBoost. UCS and GAssist are based on a non-fuzzy...

This paper proposes Pitts-DNF-C, a multi- objective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for classification tasks. The system is explicitly designed to only explore solutions that lead to consistent, complete, and compact rule sets without redundancies and inconsistencies. The behavior of the system...

This publication is the fruit of a collaborative research between academics from the marketing and the artificial intelligence fields. It presents a brand new methodology to be applied in marketing (causal) modeling. Specifically, we apply it to a consumer behavior model used for the experimentation. The characteristics of the problem (with uncerta...

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang ~TSK! rules. This allow us to start from prototypes con-sidering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm bas...

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform online reinforcement learning by means of Michigan-style fuzzy rule systems, this issue becomes even more difficult. Indeed, rule generalization (de...

This paper presents a methodology for the design of fuzzy controllers with good interpretability in mobile robotics. It is composed of a technique to automatically generate a training data set plus an efficient algorithm to learn fuzzy controllers. The proposed approach obtains a highly interpretable knowledge base in a very reduced time, and the d...

When a flexible fuzzy rule structure such as those with antecedent in conjunctive normal form is used, the interpretability of the obtained fuzzy model is significantly improved. However, some important problems appear related to the interaction among this set of rules. Indeed, it is relatively easy to get inconsistencies, lack of completeness, red...

This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. Fuzzy-UCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a set of real-world problems, and compared to...

Multicriteria genetic algorithms can produce fuzzy models with a good balance between their precision and their complexity. The accuracy of a model is usually measured by the mean squared error of its residual. When vague training data is used, the residual becomes a fuzzy number, and it is needed to optimize a combination of crisp and fuzzy object...

An abstract is not available.

Summary The present paper tries to point out the necessity that firms focused on consumer markets have to understand and predict in a higher efficiently manner the behaviour of their target population. Thus, it is convenient that models of consumer behaviour in which firms are based to take their decisions to be closer to what a real Marketing Mana...

This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. Fuzzy-UCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a large collection of real-world problems, and...

The work presents a new evolutionary algorithm designed for continuous optimization. The algorithm is based on evolution of proba- bility density functions, which focus on the most promising zones of the domain of each variable. Several mechanisms are included to self-adapt the algorithm to the feature of the problem. By means of an experi- mental...

When questionnaires are designed, each factor un-der study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we repre...

One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature. We will call these approaches as basic refinement approaches. In thi...

For certain problems of casual modeling in marketing, the information is obtained by means of questionnaires. When these questionnaires include more than one item for each observable variable, the value of this variable can not be assigned a number, but a potentially scattered set of values. In this paper, we propose to represent the information co...

A methodology for learning behaviors in mobile robotics has been developed. The algorithm is based on obtaining co-operative rules with weights, and uses a genetic algorithm to do the combinatorial search. The methodology has been em-ployed to learn the wall-following behavior, and the obtained controller has been tested using the Nomad 200 simulat...

Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic der- ivation of the linguistic fuzzy rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this art...

In this paper, we propose the use of weighted linguistic fuzzy rules in combination with a rule selection process to develop accurate fuzzy logic controllers dedicated to the intelligent control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. To do so, a genetic optimization proces...

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely developed task. It involves adjusting some of the components of the knowledge base without completely redefining it. This contribution introduces a genetic tuning process for jointly fitting the fuzzy rule symbolic representations and the meaning of the invol...

The paper introduces a novel problem based on causal modeling in marketing where knowledge discovery is able to provide useful results (as shown in a real-world application). The problem features (with uncertain data and available expert knowledge) and the proposed multiobjective op- timization approach makes genetic fuzzy systems to be a good fram...

The work presents the design of a fuzzy controller for the wall-following behavior in mobile robotics using the COR (cooperative rules) methodology with ant colony optimization. The system has been tested in several simulated environments using the Nomad 200 robot software, and compared with other controller based on genetic algorithms. The propose...

The issue of rule generalization has received a great deal of attention in the discrete-valued learning classifier system field. Within it, the accuracy-based XCS system is currently the main reference. The same issue does not appear to have re-ceived a similar level of attention in the case of the fuzzy classifier system. It may be due to the diff...

Within the field of linguistic fuzzy modeling with fuzzy rule-based sys-tems, the automatic derivation of the knowledge base from numerical data is an important task. In this contribution, we propose a new ap-proach to automatically learn the whole knowledge base, combining two different strategies for rules derivation and fuzzy partitions defi-nit...

Complex models have been traditionally and increasingly used by both marketing academics and practitioners to represent and understand consumer behaviour. Thus, we firstly pose that models of consumer behaviour firms use to make in which firms are based to take their decisions must be close to what a real Marketing Management Support System should...

This paper presents an evolutionary learning process for linguistic modeling with weighted double-consequent fuzzy rules. These kinds of fuzzy rules are used to improve the linguistic modeling, with the aim of introducing a trade-off between interpretability and precision. The use of weighted double-consequent fuzzy rules makes more complex the mod...

This paper presents the use of genetic algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. This problem has some specific restrictions that make it very particular and complex because of the large time...

This work presents the use of local fuzzy prototypes as a first approximation to ob-tain accurate local semantics-based Takagi-Sugeno rules. A two-stage evolutionary al-gorithm considering the interaction between input and output variables has been devel-oped. Firstly, it performs a local iden-tification of prototypes, and then, a post-processing s...

In this work we propose the hybridization of two techniques to improve the cooperation among the fuzzy rules: the use of rule weights and the Cooperative Rules learning methodology. To do that, the said methodology is extended to include the learning of rule weights within the rule cooperation paradigm. Considering these kinds of techniques could r...

The chapter introduces a simple learning methodology, the cooperative rules (COR) one, that improves the accuracy of linguistic fuzzy models preserving the highest interpretability. Its operation mode involves a combinatorial search of fuzzy rules performed over a set of previously generated candidate ones. The accuracy is achieved by developing a...

This work presents the use of genetic algorithms for the optimization and control of Heating, Ventilating and Air Conditioning (HVAC) systems developing smartly tuned fuzzy logic controllers for energy efficiency and overall performance of these systems. An optimum operation of the HVAC systems is a necessary condition for minimizing energy consump...

System modeling with fuzzy rule-based systems (FRBSs), Le. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (main...

Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is the capability to express the real system behavior in a comprehensible way, and accuracy, which is the capability to faithfully represent the real system. In this framework, one of the most important areas is linguistic fuzzy modeling, where the legibility...

In this work we extendthe Cooperative Rules learning methodology to improve simple linguistic fuzzy models, including the learning of rule weights within the rule cooperation paradigm. Considering these kinds of techniques could result in important improvements of the system accuracy, maintaining the interpretability to an acceptable level.

This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models: the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those that are best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace, as ad-hoc dat...