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A real coded MOGA for mining classification rules with missing attribute values

Published: 12 February 2011 Publication History

Abstract

Classification rule mining is one of the important data mining tasks. Optimized Rule Set (ORS) generation is a major challenge. Multi Objective Genetic Algorithm (MOGA) has been used to search available data effectively and among many objectives instead of single objective with its real coded elitist version along with special operator. Some Data Sets (DSs) having missing attribute values. In some of their earlier work researchers are either considered DS without any missing attributes values or eliminated records having missing attribute values at data preprocessing phase, considered missing values as one category of value, replaced missing values with the most common value of the attribute or assigned probability to each of the possible values to replace missing values. In this work these are not required. During training and testing phase attributes having valid values have been used for ORS generation and testing.

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  • (2019)Knowledge building through optimized classification rule set generation using genetic based elitist multi objective approachNeural Computing and Applications10.1007/s00521-017-3042-431:2(845-855)Online publication date: 17-May-2019
  • (2013)Evolution of Genetic Algorithms in Classification Rule MiningHandbook of Research on Computational Intelligence for Engineering, Science, and Business10.4018/978-1-4666-2518-1.ch013(328-363)Online publication date: 2013

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    cover image ACM Other conferences
    ICCCS '11: Proceedings of the 2011 International Conference on Communication, Computing & Security
    February 2011
    656 pages
    ISBN:9781450304641
    DOI:10.1145/1947940
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    Published: 12 February 2011

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    Author Tags

    1. MOGA
    2. classification rule mining
    3. missing attribute values

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    • (2019)Knowledge building through optimized classification rule set generation using genetic based elitist multi objective approachNeural Computing and Applications10.1007/s00521-017-3042-431:2(845-855)Online publication date: 17-May-2019
    • (2013)Evolution of Genetic Algorithms in Classification Rule MiningHandbook of Research on Computational Intelligence for Engineering, Science, and Business10.4018/978-1-4666-2518-1.ch013(328-363)Online publication date: 2013

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