Practical pattern classiication and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classiied. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subse...

The analysis of large biological data sets that arise in gene expression or proteomics experiments often involves the selection of a subset of the available features that supports efficient classification. Finding multiple, distinct solutions to the feature subset selection problem may lead to increased biological insights. In this paper we address the problem of finding multiple solutions to t...

Practical pattern classi cation and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classi ed. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subse...

land cover classification is one of the most important applications of polarimetric radar images, especially in urban areas. there are numerous features that can be extracted from these images for the use of their high potential, hence feature selection plays an important role in polsar image classification. in this study, three main steps are used to improve the classification: 1) feature extr...

pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. the surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (seldi-tof ms) has been used to generate proteomic profiles from biological fluids. mass spectrometry yields redundant noisy data that the most data points are irrelevant features ...