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.