A FUZZY HYPERCUBE MODEL FOR CLASSIFICATION PROBLEMS

Abstract

Classification problems are naturally characterized by uncertainty, subjectivity, imprecision and ambiguity. Thus, in designing classification models, mathematical methods that are able to satisfactorily deal with uncertainty, ambiguity and subjectivity are essential. Although fuzzy set theory is a very convenient mathematical tool for treating vagueness and ambiguity due to redundant and irrelevant features, and poor class definition, the existing fuzzy entropy based techniques can not effectively deal with ambiguity. This thesis presents a geometrical fuzzy similarity classifier which allows us not only reduce complexity of classification problems by removing redundant and irrelevant features, but also estimate ambiguity in class assignment using measures of fuzzy specificity. The model was tested using 4 benchmark datasets from University of California Irvine (UCI) machine learning repository yielding very attractive results. With Dermatology data set, a mean classification accuracy of 98:21% was obtained with only 24 features as compared with 97:82% with 34 features. Uncertainty associated with class assignment is also reported. Miss-classified samples display high average uncertainty as compared to those correctly classified.