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Incremental Approach to Classification Learning

Incremental Approach to Classification Learning
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Author(s): Xenia Alexandre Naidenova (Research Centre of Military Medical Academy – Saint Petersburg, Russia)
Copyright: 2019
Pages: 13
Source title: Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 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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