Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems

Abstract

Abstract-We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if-then rules and fuzzy reasoning for pattern classification problems. Then we explain a genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if-then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if-then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some well-known test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.

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

“…In the previous years, various genetic learning approaches have been considered for creating GFRBCSs 22,[27][28][29][30][31] , each exhibiting different benefits and drawbacks. Here we concentrate on the so-called iterative rule learning (IRL) approach [29][30][31] , which is the methodology followed by the proposal of this paper.…”

Recursive Learning of Genetic Algorithm Featuring Incremental Attribute Learning for Higher Dimensional Classification Problems

“…Moreover, comparing with artificial neural networks, naive Bayes classifier, decision trees, supportive vector machines, and other popular machine learning algorithms [1][5] [15][17], traditional GA classifier have their weaknesses in efficiency and classification accuracy, especially when confronting higher dimensional problems.…”

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

“…The definition in (15) has been used in many fuzzy rule-based classification systems in our former studies (e.g., Ishibuchi, Nakashima and Murata (1999), ) since Ishibuchi, Nozaki and Tanaka (1992). On the other hand, the definition in (16) has been used in some recent studies (e.g., Ishibuchi and Yamamoto (2003b)).…”