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...

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...

The feature selection allows to choose P features among M (P<M) and thus to reduce the representation space of data. This process is increasingly useful because of the databases size increase. Therefore we propose a method based on preferences aggregation. It is an hybrid method between filter and wrapper approaches.

High dimensionnal data is a challenge for the KDD community. Feature Selection (FS) is an efficient preprocessing step for dimensionnality reduction thanks to the removal of redundant and/or noisy features. Few and mostly recent FS methods have been proposed for clustering. Furthermore, most of them are ”wrapper” methods that require the use of clustering algorithms for evaluating the selected ...

In a growing number of domains the data collected has a large number of features. This poses a challenge to classical pattern recognition techniques, since the number of samples often is still limited with respect to the feature size. Classical pattern recognition methods suffer from the small sample size, and robust classification techniques are needed. In order to reduce the dimensionality of...

Selecting an optimal subset from original large feature set in the design of pattern classi"er is an important and di$cult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and boundmethod, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential...

In many classification problems, and in particular in medical domains, it is common to have an unbalanced class distribution. This pose problems to classifiers as they tend to perform poorly in the minority class which is often the class of interest. One commonly used strategy that to improve the classification performance is to select a subset of relevant features. Feature selection algorithms...