Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24282
Title: Rule induction from numerical data based on rough sets theory
Authors: Zhao, S
Ng, WWY
Tsang, ECC
Yeung, DS
Chen, D
Keywords: Computational complexity
Fuzzy set theory
Knowledge based systems
Matrix algebra
Rough set theory
Issue Date: 2006
Publisher: IEEE
Source: 2006 International Conference on Machine Learning and Cybernetics, 13-16 August 2006, Dalian, China, p. 2294-2299 How to cite?
Abstract: To induce rules from numerical data by rough sets, there are two kinds of methods. One is to discretize the original data and then apply the crisp rough sets models. Here the rough sets models which can only deal with the nominal data are called crisp rough sets models. The other is to fuzzify the original data and then apply fuzzy rough sets models. There are some problems on both of these methods on rules induction such as information loss after discretization or increasing of data size after fuzzification. In this paper we make an attempt to propose one method to induce rules without discretization or fuzzification. Firstly the indiscernibility relation which is the underlining concept of rough sets is redefined as the similarity relation. Subsequently, the concepts of knowledge reduction are proposed based on the similarity relation. Finally, the numerical experiments show that our method is feasible and effective
URI: http://hdl.handle.net/10397/24282
ISBN: 1-4244-0061-9
DOI: 10.1109/ICMLC.2006.258676
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