Systems For Pattern Classification Problems Research Articles

Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems

We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.

Voting in fuzzy rule-based systems for pattern classification problems

In this paper, we examine two kinds of voting schemes in fuzzy rule-based systems for pattern classification problems. One is the voting by multiple fuzzy if-then rules in a single fuzzy rule-based classification system. The other is the voting by multiple fuzzy rule-based classification systems. First, we discuss the voting by multiple fuzzy if-then rules, which is used as a fuzzy reasoning method for classifying input patterns in a single fuzzy rule-based classification system. The performance of the voting by multiple fuzzy if-then rules is examined by computer simulations on the iris data. Next, we discuss the voting by multiple fuzzy rule-based classification systems. Three voting methods (i.e., a perfect unison rule, a majority rule, and a weighted voting rule) are used for combining classification results by multiple fuzzy rule-based classification systems. Finally, we compare the performance of fuzzy rule-based classification systems with that of other classification methods such as neural networks and statistical techniques by computer simulations on some well-known test problems.

Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes

In this paper, various methods are introduced for improving the ability of fuzzy classifier systems to automatically generate fuzzy if-then rules for pattern classification problems with continuous attributes. First, we describe a simple fuzzy classifier system where a randomly generated initial population of fuzzy if-then rules is evolved by typical genetic operations, such as selection, crossover, and mutation. By computer simulations on a real-world pattern classification problem with many continuous attributes, we show that the search ability of such a simple fuzzy classifier system is not high. Next, we examine the search ability of a hybrid algorithm where a learning procedure of fuzzy if-then rules is combined with the fuzzy classifier system. Then, we introduce two heuristic procedures for improving the performance of the fuzzy classifier system. One is a heuristic rule generation procedure for an initial population where initial fuzzy if-then rules are directly generated from training patterns. The other is a heuristic population update procedure where new fuzzy if-then rules are generated from misclassified and rejected training patterns, as well as from existing fuzzy if-then rules by genetic operations. By computer simulations, we demonstrate that these two heuristic procedures drastically improve the search ability of the fuzzy classifier system. We also examine a variant of the fuzzy classifier system where the population size (i.e., the number of fuzzy if-then rules) varies depending on the classification performance of fuzzy if-then rules in the current population.