Membership-margin based feature selection for mixed type and high-dimensional data: Theory and applications

The present paper describes a new feature weighting method based on a membership margin. Distinctive properties of the proposed method include its capability to process problems characterized by mixed-type data (quantitative, qualitative and interval) as well as a huge number of features. The key idea is to map simultaneously all the features of different types into a common space; the membership space. Once all features are represented in a homogeneous space, a feature weighting task can be performed in unified way. This weighting approach is integrated here within a fuzzy classifier through a fuzzy rule weighted concept in order to improve its performance. Each antecedent fuzzy set in the fuzzy if-then rule is weighted to characterize the importance of each proposition and therefore its corresponding feature. Weight estimation process is based on membership margin maximization to estimate a fuzzy weight of each feature in the membership space. Experiments on low and high dimensional real-world datasets demonstrate that the proposed approach can improve significantly the performance of the fuzzy rule-based as well as other state of the art classifiers and can even outperform classical feature weighting approaches. In particular, we show that this approach can yield meaningful results on two real-world applications for cancer prognosis and industrial process diagnosis.