A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning

By Jesús Alcalá-Fdez, Rafael Alcalá, Francisco Herrera. IEEE transactions on fuzzy systems, 2011

384 citations recorded by the source corpus.

The inductive learning of fuzzy rule-based classification systems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. This growth makes the learning process more difficult and, in most cases, it leads to problems of scalability (in terms of the time and memory consumed) and/or complexity (with respect to the number of rules obtained and the number of variables included in each rule). In this paper, we propose a fuzzy association rule-based classification method for high-dimensional problems, which is based on three stages to obtain an accurate and compact fuzzy rule-based classifier with a low computational cost. This method limits the order of the associations in the association rule extraction and considers the use of subgroup discovery, which is based on an improved weighted relative accuracy measure to preselect the most interesting rules before a genetic postprocessing process for rule selection and parameter tuning. The results that are obtained more than 26 real-world datasets of different sizes and with different numbers of variables demonstrate the effectiveness of the proposed approach.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/7a39410003340ea15104a22507ac948ce2a8acf9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zeshui Xu
2. Francisco Herrera
3. Edurne Barrenechea Tartas
4. Humberto Bustince
5. Javier Montero
6. Javier Fernández
7. Benjamín René Callejas Bedregal
8. B. De Baets

In Information Systems

1. Zeshui Xu
2. Francisco Herrera
3. Edurne Barrenechea Tartas
4. Humberto Bustince
5. Bahram Farhadinia

Citations in the corpus

• A Historical Account of Types of Fuzzy Sets and Their Relationships (2016, methodology): “The experimental results presented in [128] show that the approach using IVFSs (named IVTURS) improves the results of two state-of-the-art fuzzy classifiers like FARCHD [2] and FURIA [77].”
• A preliminary study on deep learning for predicting social insurance payment behavior (2017, methodology): “[1] proposed a fuzzy rule-based classification method for the highdimensional problems.”