A Grey-Rough Set Approach for Interval Data Reduction of Attributes

  • Authors:

  • Daisuke Yamaguchi;Guo-Dong Li;Masatake Nagai

  • Affiliations:

  • Graduate School of Kanagawa University, Department of Engineering, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama City, 221---8686, Japan;Graduate School of Kanagawa University, Department of Engineering, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama City, 221---8686, Japan;Kanagawa University, Faculty of Engineering, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama City, 221---8686, Japan

  • Venue:
  • RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Reduction in rough set theory is useful to compact given attributes of large-scale decision tables in data mining. In this paper a new method called grey-rough reduction is proposed for decision tables containing non-interval data and interval data complexly called grey-decision tables. First of all, a grey-rough approximation is introduced after summarized grey numbers, their operations and functions. Two sorts of reduction based on grey-rough sets, a basic approach and advanced approach are proposed with several illustrative examples. Three experiments, compatibility with the classical model, an application of the basic approach to decision-making and influence of the parameter in the advanced approach are shown. The advantages of the proposal are (1) it is compatible with the classical reduction model for non-interval data, (2) it is useful for complex decision tables and (3) it provides a possible reduction of attributes with a parameter by the advanced approach.