Sanz Delgado, José Antonio
Loading...
Email Address
person.page.identifierURI
Birth Date
Job Title
Last Name
Sanz Delgado
First Name
José Antonio
person.page.departamento
Estadística, Informática y Matemáticas
person.page.instituteName
ISC. Institute of Smart Cities
ORCID
person.page.observainves
person.page.upna
Name
- Publications
- item.page.relationships.isAdvisorOfPublication
- item.page.relationships.isAdvisorTFEOfPublication
- item.page.relationships.isAuthorMDOfPublication
1 results
Search Results
Now showing 1 - 1 of 1
Publication Open Access CFM-BD: a distributed rule induction algorithm for building compact fuzzy models in Big Data classification problems(IEEE, 2020) Elkano Ilintxeta, Mikel; Sanz Delgado, José Antonio; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Galar Idoate, Mikel; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y MatemáticasInterpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule based classifiers have not been able to maintain the good tradeoff between accuracy and interpretability that has characterized these techniques in non-Big-Data environments. The most accurate methods build models composed of a large number of rules and fuzzy sets that are too complex, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: Preprocessing based on the probability integral transform theorem; rule induction inspired by CHI-BD and Apriori algorithms; and rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than three antecedents, employing five linguistic labels for all variables.