A novel fuzzy classifier based on product aggregation operator

The present article proposes a fuzzy set-based classifier with a better learning and generalization capability. The proposed classifier exploits the feature-wise degree of belonging of a pattern to all classes, generalization in the fuzzification process and the combined class-wise contribution of features effectively. The classifier uses a ππ-type membership function and product aggregation reasoning rule (operator). Its effectiveness is verified with two conventional (completely labeled) data sets and two remote sensing images (partially labeled data sets). The proposed classifier is compared with similar fuzzy methods. Different performance measures are used for quantitative evaluation of the proposed classifier.