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Incremental Approach to Classification Learning

Incremental Approach to Classification Learning

Xenia Alexandre Naidenova
Copyright: © 2018 |Pages: 11
ISBN13: 9781522522553|ISBN10: 1522522557|EISBN13: 9781522522560
DOI: 10.4018/978-1-5225-2255-3.ch017
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MLA

Naidenova, Xenia Alexandre. "Incremental Approach to Classification Learning." Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 191-201. https://doi.org/10.4018/978-1-5225-2255-3.ch017

APA

Naidenova, X. A. (2018). Incremental Approach to Classification Learning. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 191-201). IGI Global. https://doi.org/10.4018/978-1-5225-2255-3.ch017

Chicago

Naidenova, Xenia Alexandre. "Incremental Approach to Classification Learning." In Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., 191-201. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch017

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Abstract

An approach to incremental classification learning is proposed. Classification learning is based on approximation of a given partitioning of objects into disjoint blocks in multivalued space of attributes. Good approximation is defined in the form of good maximally redundant classification test or good formal concept. A concept of classification context is introduced. Four situations of incremental modification of classification context are considered: adding and deleting objects and adding and deleting values of attributes. Algorithms of changing good concepts in these incremental situations are given and proven.

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