Classification Confidence Of Fuzzy Rule-Based Classifiers

In this paper we first introduce the concept of classification confidence in fuzzy rule-based classification. Classification confidence shows the strength of classification for an unseen pattern. Low classification confidence for an unseen pattern means that the classification of that pattern is not very clear compared to that with high classification confidence. Then we focus on the minimum classification confidence for fuzzy rule-based classifiers using the classification confidence. The minimum classification confidence represents the worst classification among given training patterns. Some discussion on assigning a weight to training pattern is given to show that cost-sensitive fuzzy rule-based classifiers are advantageous for producing a large minimum-confidence classifiers. A series of experiments are done in order to show that reasonable classification boundaries can be obtained by cost-sensitive fuzzy rule-based classifiers if appropriate weights are assigned to training patterns.