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Incremental Approach to Classification Learning

Incremental Approach to Classification Learning

Xenia Alexandre Naidenova
Copyright: © 2019 |Pages: 13
ISBN13: 9781522573685|ISBN10: 1522573682|EISBN13: 9781522573692
DOI: 10.4018/978-1-5225-7368-5.ch010
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MLA

Naidenova, Xenia Alexandre. "Incremental Approach to Classification Learning." Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2019, pp. 123-135. https://doi.org/10.4018/978-1-5225-7368-5.ch010

APA

Naidenova, X. A. (2019). Incremental Approach to Classification Learning. In M. Khosrow-Pour, D.B.A. (Ed.), Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction (pp. 123-135). IGI Global. https://doi.org/10.4018/978-1-5225-7368-5.ch010

Chicago

Naidenova, Xenia Alexandre. "Incremental Approach to Classification Learning." In Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction, edited by Mehdi Khosrow-Pour, D.B.A., 123-135. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7368-5.ch010

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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 disjointed 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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