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Optimization Problem of k-NN Classifier in DNA Microarray Methods

  • Urszula BentkowskaEmail author
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 378)

Abstract

The microarrays are a particularly interesting tool in modern molecular biology, not only because of the wide spectrum of applications such as analysis of the genome structure, profile gene expression, genotyping, sequencing, but also due to the possibility of testing a large number of objects in one experiment (cf. [1, 2]). However, the identification of relevant information from a huge amount of data obtained using microarrays requires the use of sophisticated bioinformatic methods. Clustering methods or machine learning algorithms are applied. However, when such methods are used, there is a problem of lowering their performance on test data due to the large number of attributes (columns). In this chapter microarray methods are applied for identification of marker genes. Our aim is to show that the quality of classification in the case of large number of attributes may be improved while using the microarray methods and interval modeling. There will be considered the so called vertical decomposition of a table representing a given data set.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Mathematics and Natural SciencesUniversity of RzeszówRzeszówPoland

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