Abstract
discernibility matrix and binary discernibility matrix method is easy to understand and design, which has aroused great concern by many scholar. Research shows that the two methods produce a large number of repeated and useless elements (if A is the subset of B, B is the useless element of A) on the fly. These repeated and useless elements occupy a lot of space and will affect the efficiency of the algorithm. If we delete these elements, the storage is much less than before, and the algorithm will be increased. For this purpose, professor Yang Ming give the definition of enriching discernibility matrix [3],which all the discernibility elements are not repetition and mutually exclusive. Some scholars adopt the method of comparison between every two discernibility elements to get the enriching discernibility matrix. Some present the algorithm, every nonempty entry of a discernibility matrix is stored one path in the enriching tree and a lot of nonempty entries share one path or sub-path. However, these algorithms only delete part of the useless elements in spite of lower storage space. In this paper, we put forward discernibility matrix enriching and Boolean- And algorithm for attributes Reduction. The algorithm is easy to understand and easy to design. The Analysis Experiment and Experimental Comparison show the algorithm is feasible and effective.
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Xu, Z., Wang, T., Zhu, J., Zhang, X. (2014). Discernibility Matrix Enriching and Boolean-And Algorithm for Attributes Reduction. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_52
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DOI: https://doi.org/10.1007/978-3-319-14717-8_52
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