Tomasz Łukaszuk

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Feature selection is one of active research area in pattern recognition or data mining methods (Duda et al., 2001). The importance of feature selection methods becomes apparent in the context of rapidly growing amount of data collected in contemporary databases (Liu & Motoda, 2008). Feature subset selection procedures are aimed at neglecting as large as(More)
Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition and classification applications. It is particularly important when a small number of cases is represented in a highly dimensional feature space. The method of the feature selection based on minimisation of a special criterion function (convex and(More)
Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed linear separability (RLS) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic)(More)
Most of the commonly known feature selection methods focus on selecting appropriate predictors for image recognition or generally on data mining issues. In this paper we present a comparison between widely used Recursive Feature Elimination (RFE) with resampling method and the Relaxed Linear Separability (RLS) approach with application to the analysis of(More)
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