Tomasz Łukaszuk

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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)
In recent years, there has been a huge explosion of genomic data. It is because of progress of high-performance biotechnology, such as RNA gene expression microarray. These large genomic data sets are rich in information and often contain much more information than scientists who generated the data could expect. The information contained in genomic data(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)
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)
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