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Finding Essential Attributes in Binary Data

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Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents (IDEAL 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

Abstract

Given a data set, consisting of n-dimensional binary vectors of positive and negative examples, a subset S of the attributes is called a support set if the positive and negative examples can be distinguished by using only the attributes in S. In this paper we consider several selection criteria for evaluating the “separation power” of supports sets, and formulate combinatorial optimization problems for finding the “best and smallest” support sets with respect to such criteria. We provide efficient heuristics, some with a guaranteed performance rate, for the solution of these problems, analyze the distribution of small support sets in random examples, and present the results of some computational experiments with the proposed algorithms.

This work was partially supported by the Grants in Aid by the Ministry of Education, Science, Sports and Culture of Japan (Grants 09044160 and 10205211). The visit of the first author to Kyoto University (January to March, 1999) was also supported by this grant (Grant 09044160). The research of the first and third authors were supported in part by the Office of Naval Research (Grant N00014-92-J-1375). The first author thanks also the National Science Foundation (Grant DMS 98-06389) and DARPA (Contract N66001-97-C-8537) for partial support.

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Author information

Authors and Affiliations

  1. RUTCOR, Rutgers University, 640 Bartholomew Road, 08854-8003, Piscataway, NJ, USA

    Endre Boros

  2. Graduate School of Information Science, Nara Institute of Science and Technology, 630-0101, Nara, Japan

    Takashi Horiyama

  3. Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, 606-8501, Kyoto, Japan

    Toshihide Ibaraki & Mutsunori Yagiura

  4. Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University, 560-8531, Toyonaka, Osaka, Japan

    Kazuhisa Makino

Authors
  1. Endre Boros
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  2. Takashi Horiyama
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  3. Toshihide Ibaraki
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  4. Kazuhisa Makino
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  5. Mutsunori Yagiura
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Kwong Sak Leung  & Lai-Wan Chan  & 

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

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© 2000 Springer-Verlag Berlin Heidelberg

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Boros, E., Horiyama, T., Ibaraki, T., Makino, K., Yagiura, M. (2000). Finding Essential Attributes in Binary Data. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_20

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  • DOI: https://doi.org/10.1007/3-540-44491-2_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

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