Abstract
Attribute reduction is a combinatorial optimization problem in data mining that aims to find minimal reducts from large set of attributes. The problem is exacerbated if the number of instances is large. Therefore, this paper concentrates on a double treatment iterative improvement algorithm with intelligent selection on composite neighbourhood structure to solve the attribute reduction problems and to obtain near optimal reducts. The algorithm works iteratively with only accepting an improved solution. The proposed approach has been tested on a set of 13 benchmark datasets taken from the University of California, Irvine (UCI) machine learning repository in line with the state-of-the-art methods. The 13 datasets have been chosen due to the differences in size and complexity in order to test the stability of the proposed algorithm. The experimental results show that the proposed approach is able to produce competitive results for the tested datasets.
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Kifah, S., Abdullah, S., Arajy, Y.Z. (2015). Intelligent Double Treatment Iterative Algorithm for Attribute Reduction Problems. In: Phon-Amnuaisuk, S., Au, T. (eds) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-319-13153-5_10
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DOI: https://doi.org/10.1007/978-3-319-13153-5_10
Publisher Name: Springer, Cham
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