International Journal of Computational Intelligence Systems

Volume 5, Issue 2, April 2012

1. Special Issue on Evolutionary Fuzzy Systems

Rafael Alcalá, Yusuke Nojima, Hisao Ishibuchi, Francisco Herrera
Pages: 209 - 211

2. An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

Antonio González, Raúl Pérez, Yoel Caises, Enrique Leyva
Pages: 212 - 230
Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the...

3. A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

Krzysztof TrawiÅ„ski, Oscar Cordón, Arnaud Quirin
Pages: 231 - 253
In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature...

4. Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

DimitrisG. Stavrakoudis, GeorgiaN. Galidaki, IoannisZ. Gitas, JohnB. Theocharis
Pages: 254 - 275
This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when...

5. Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

AnaM. Palacios, Luciano Sánchez, Inés Couso
Pages: 276 - 296
Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling...

6. A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

AntonioA. Márquez, FranciscoA. Márquez, Antonio Peregrín
Pages: 297 - 321
This paper proposes a mechanism that helps improve the interpretability of linguistic fuzzy ruled based systems with common adaptive defuzzification methods. Adaptive defuzzification significantly improves the system accuracy, but introduces weights associated with each rule of the rule base, decreasing...

7. Improving Transparency in Approximate Fuzzy Modeling Using Multi-objective Immune-Inspired Optimisation

Jun Chen, Mahdi Mahfouf
Pages: 322 - 342
In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, prediction accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts...

8. Finding Pareto-front Membership Functions in Fuzzy Data Mining

Chun-Hao Chen, Tzung-Pei Hong, VincentS. Tseng
Pages: 343 - 354
Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results....

9. Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

C.J. Carmona, P. González, M.J. Gacto, M.J. del Jesus
Pages: 355 - 367
The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm –one of the most representative evolutionary...

10. Automatic Laser Pointer Detection Algorithm for Environment Control Device Systems Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based Systems

F. Chávez, F. Fernández, M.J. Gacto, R. Alcalá
Pages: 368 - 386
In this paper we propose a new approach for laser-based environment device control systems based on the automatic design of a Fuzzy Rule-Based System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs and increasing...

11. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

Ángela Nebot, Francisco Mugica, Félix Castro, Jesús Acosta
Pages: 387 - 402
In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn...