Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing

This paper presents a multiobjective genetic algorithm which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The evolutionary algorithm follows a multiobjective approach in order to optimize in a suitable way the different quality measures used in this kind of problems. Experimental evaluation of the algorithm, applying it to a market problem studied in the University of Mondragón (Spain), shows the validity of the proposal. The application of the proposal to this problem allows us to obtain novel and valuable knowledge for the experts.Spanish Ministry of Science and TechnologyFEDER TIC-2005-08386-C05-01 and TIC-2005- 08386-C05-03TIN2004-20061-E and TIN2004-21343-

Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms

In recent years, the increasing interest in fuzzy rough set theory has allowed the definition of novel accurate methods for feature selection. Although their stand-alone application can lead to the construction of high quality classifiers, they can be improved even more if other preprocessing techniques, such as instance selection, are considered. With the aim of enhancing the nearest neighbor classifier, we present a hybrid algorithm for instance and feature selection, where evolutionary search in the instances’ space is combined with a fuzzy rough set based feature selection procedure. The preliminary results, contrasted through nonparametric statistical tests, suggest that our proposal can improve greatly the performance of the preprocessing techniques in isolation.Project TIN2008-06681-C06-01Spanish Ministry of EducationResearch Foundation - Flander

A proposal on reasoning methods in fuzzy rule-based classification systems

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism to the set of rules. Finally, to show the increase of the system generalization capability provided by the proposed FRMs, we point out some results obtained by their integration in a fuzzy rule generation process.CICYT TIC96-077

Replacing pooling functions in Convolutional Neural Networks by linear combinations of increasing functions

Traditionally, Convolutional Neural Networks make use of the maximum or arithmetic mean in order to reduce the features extracted by convolutional layers in a downsampling process known as pooling. However, there is no strong argument to settle upon one of the two functions and, in practice, this selection turns to be problem dependent. Further, both of these options ignore possible dependencies among the data. We believe that a combination of both of these functions, as well as of additional ones which may retain different information, can benefit the feature extraction process. In this work, we replace traditional pooling by several alternative functions. In particular, we consider linear combinations of order statistics and generalizations of the Sugeno integral, extending the latter’s domain to the whole real line and setting the theoretical base for their application. We present an alternative pooling layer based on this strategy which we name ‘‘CombPool’’ layer. We replace the pooling layers of three different architectures of increasing complexity by CombPool layers, and empirically prove over multiple datasets that linear combinations outperform traditional pooling functions in most cases. Further, combinations with either the Sugeno integral or one of its generalizations usually yield the best results, proving a strong candidate to apply in most architectures.Tracasa Instrumental (iTRACASA), SpainGobierno de Navarra-Departamento de Universidad, Innovacion y Transformacion Digital, SpainSpanish Ministry of Science, Spain PID2019-108392GB-I00Andalusian Excellence project, Spain PID2019-108392GB-I00Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) PC095-096Fundacao de Amparo a Ciencia e Tecnologia do Estado do Rio Grande do Sul (FAPERGS) P18-FR-4961 301618/2019-4 19/2551-000 1279-

Algunas ideas sobre la investigación (reflexiones y consejos : tesis doctoral, metodología de la investigación y escritura de artículos científicos

En esta conferencia presentaré mis reflexiones sobre la organización del trabajo de investigación y la producción científica, analizando las decisiones que día a día tenemos que tomar en el seno de un grupo de investigación: Introducción, investigación y creatividad . Organización de un grupo de investigación . Selección de las líneas de investigación . Preparación de nuevos doctorandos . Elaboración de un buen artículo y selección del foro de publicació

A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study

The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models

An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons

In a recently published paper in JMLR, Demsar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n*n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures.This research has been supported by the project TIN2005-08386-C05-01. S. García holds a FPU scholarship from Spanish Ministry of Education and Science

A Trust Risk Dynamic Management Mechanism Based on Third-Party Monitoring for the Conflict-Eliminating Process of Social Network Group Decision Making

This work was supported in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_0507; in part by the Fundamental Research Funds for the Central Universities under Grant B200203165 and Grant B220203013; in part by the National Natural Science Foundation of China (NSFC) under Grant 71871085; in part by the National Natural Science Foundation of Jiangsu Province under Grant BK20210634; in part by the Startup Foundation for Introducing Talent of NUIST under Grant 1521182101004; and in part by the China Scholarship Council under Grant 202106710123.Every decision may involve risks. Real-world risk issues are usually supervised by third parties. Decision-making may be affected by the absence of sufficient or reasonable trust or to the opposite, an unconditional, excessive, or blind trust, which is called trust risks. The conflict-eliminating process (CEP) aims to facilitate satisfactory consensus by decision makers (DMs) through continuous reconciliation between their opinion differences on the subject matter. This article addresses trust risks in CEP of social network group decision making (SNGDM) through third-party monitoring. A trust risk analysis-based conflict-eliminating model for SNGDM is developed. It is assumed that a third-party agency monitors the DMs’ credibility and performance, which is recorded in an objective evaluation matrix and multi-attribute trust assessment matrix (MTAM). A trust risk measurement methodology is proposed to classify the DMs’ different trust risk types and to measure the trust risk index (TRI) of a group of DMs. When TRI is unacceptable, a trust risk management mechanism that controls TRI is activated. Different management policies are applicable to DMs’ different trust risk types. There are two main methods: 1) dynamically update the MTAM based on DMs’ performance and 2) provide suggestions for modifying the DM’s information with high TRI. Besides, as part of the integrated CEP, this model includes an optimization approach to dynamically derive DMs’ reliable aggregation weights from their MTAM. Simulation experiments and an illustrative example support the feasibility and validity of the proposed model for managing trust risks in CEP of SNGDM.Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX20_0507Fundamental Research Funds for the Central Universities B200203165 B220203013National Natural Science Foundation of China (NSFC) 71871085Natural Science Foundation of Jiangsu Province BK20210634Startup Foundation for Introducing Talent of NUIST 1521182101004China Scholarship Council 20210671012

Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering

Imbalance data constitutes a great difficulty for most algorithms learning classifiers. However, as recent works claim, class imbalance is not a problem in itself and performance degradation is also associated with other factors related to the distribution of the data as the presence of noisy and borderline examples in the areas surrounding class boundaries. This contribution proposes to extend SMOTE with a noise filter called Iterative-Partitioning Filter (IPF), which can overcome these problems. The properties of this proposal are discussed in a controlled experimental study against SMOTE and its most well-known generalizations. The results show that the new proposal performs better than exiting SMOTE generalizations for all these different scenarios.Regional Projects P1O-TIC-06858 P11-TIC-9704 P12-TIC-2958 NCN-2013/11/B/5T6/00963National Project TIN2011-28488Spanish Governmen