A novel discretizer for knowledge discovery approaches based on rough sets

  • Authors:

  • Qingxiang Wu;Jianyong Cai;Girijesh Prasad;T. M. McGinnity;David Bell;Jiwen Guan

  • Affiliations:

  • School of Physics and OptoElectronic Technology, Fujian Normal University, Fujian, Fuzhou, China;School of Physics and OptoElectronic Technology, Fujian Normal University, Fujian, Fuzhou, China;School of Computing and Intelligent Systems, University of Ulster, Londonderry, N.Ireland, UK;School of Computing and Intelligent Systems, University of Ulster, Londonderry, N.Ireland, UK;School of Computer Science, Queens University, Belfast, UK;School of Computer Science, Queens University, Belfast, UK

  • Venue:
  • RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
  • Year:
  • 2006

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Abstract

Knowledge discovery approaches based on rough sets have successful application in machine learning and data mining. As these approaches are good at dealing with discrete values, a discretizer is required when the approaches are applied to continuous attributes. In this paper, a novel adaptive discretizer based on a statistical distribution index is proposed to preprocess continuous valued attributes in an instance information system, so that the knowledge discovery approaches based on rough sets can reach a high decision accuracy. The experimental results on benchmark data sets show that the proposed discretizer is able to improve the decision accuracy