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A New Knowledge Reduction Algorithm Based on Decision Power in Rough Set

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Book cover Transactions on Rough Sets XII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 6190))

Abstract

Many researchers are working on developing fast data mining methods for processing huge data sets efficiently, but some current reduction algorithms based on rough sets still have some disadvantages. In this paper, we indicated their limitations for reduct generation, then a new measure to knowledge was introduced to discuss the roughness of rough sets, and we developed an efficient algorithm for knowledge reduction based on rough sets. So, we modified the mean decision power, and proposed to use the algebraic definition of decision power. To select optimal attribute reduction, the judgment criterion of decision with an inequality was presented and some important conclusions were obtained. A complete algorithm for the attribute reduction was designed. Finally, through analyzing the given example, it is shown that the proposed heuristic information is better and more efficient than the others, and the presented method in the paper reduces time complexity and improves the performance. We report experimental results with several data sets from UCI Machine Learning Repository, and we compare the results with some other methods. The results prove that the proposed method is promising, which enlarges the application areas of rough sets.

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Author information

Authors and Affiliations

  1. College of Computer and Information Technology, Henan Normal University, Xinxiang Henan, 453007, China

    Jiucheng Xu & Lin Sun

Authors
  1. Jiucheng Xu
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  2. Lin Sun
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T 5V6, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Insitute of Computing Science, Laboratory of Intelligent Decision Support Systems, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Roman Słowiński

  4. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, NS, Canada

    Pawan Lingras

  5. School of Electronics and Information Engineering, Tongji University, 201804, Shanghai, P.R. China

    Duoqian Miao

  6. Faculty of Medicine, Department of Medical Informatics, 89-1 Enya-cho, Shimane University, 693-8501, Izumo, Japan

    Shusaku Tsumoto

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Xu, J., Sun, L. (2010). A New Knowledge Reduction Algorithm Based on Decision Power in Rough Set. In: Peters, J.F., Skowron, A., Słowiński, R., Lingras, P., Miao, D., Tsumoto, S. (eds) Transactions on Rough Sets XII. Lecture Notes in Computer Science, vol 6190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14467-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-14467-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14466-0

  • Online ISBN: 978-3-642-14467-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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