Effective classification using feature selection and fuzzy integration
Many classification problems involve features whose specificity demand some form of feature space transformation (preprocessing) coupled with post-processing consensus analysis in order to accomplish a successful discrimination between different classes. In this study, we present a new methodology, which systematically addresses these design classification issues. At the preprocessing phase we offer a new approach of stochastic feature selection. This type of feature selection, collates quadratically transformed feature subsets for presentation to a collection of respective classifiers. In the sequel, independent classification outcomes are aggregated through fuzzy integration. The motivation behind the proposed methodology is twofold. Often, only a subset of features possesses discriminatory power while the remainder has a tendency to confound the effectiveness of the underlying classifier. Quite commonly, classification based on some consensus of classification outcomes coming from a set of classifiers operating upon different feature subsets becomes more accurate than the classification results produced by any individual classifier. To illustrate this design methodology, we discuss a classification problem coming from software engineering. Here we are concerned with a dataset comprosed of features describing a collection of qualitative attributes of a software system. The experiments demonstrate that the aggregated classification results using fuzzy integration are superior to the predictions from the respective best single classifiers. (C) 2008 Elsevier B.V. All rights reserved.
fuzzy integration stochastic feature selection feature subsets fuzzy measure quantitative software engineering