Feature interaction is an important issue in feature subset selection. However, most of the existing algorithms only focus on dealing with irrelevant and redundant features. In this paper, a propositional FOIL rule based algorithm FRFS, which not only retains relevant features and excludes irrelevant and redundant ones but also considers feature interaction, is proposed for selecting feature su...

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modeled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model me...

In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for ...

Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between opcode features of malicious samples perform feature extraction, selection fusion by filtering redundant features, thus alleviating dimensional disaster problem achieving efficient identification malware families for proper classification. authors use obfuscation technology...