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CBMS
2005
IEEE

Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis

12 years 1 months ago
Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis
Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before applying a learning algorithm. Especially it is important for multidimensional heterogeneous data, presented by a large number of features of different types. Dimensionality reduction is one commonly applied approach. The goal of this paper is to study the impact of natural clustering on dimensionality reduction for classification. We compare several data mining strategies that apply dimensionality reduction by means of feature extraction or feature selection for subsequent classification. We show experimentally on microbiological data that local dimensionality reduction within natural clusters results in a better feature space for classification in comparison with the global search in terms of generalization accuracy.
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2005
Where CBMS
Authors Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen
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