Abstract
Dimension reduction of data is an important issue in the data processing and it is needed for the analysis of higher dimensional data in the application domain. Rough set is fundamental and useful to reduce higher dimensional data to lower one for the classification. We develop generation of reducts by using partial data for the classification in which their operations derive reducts without using all the data. The nearest neighbor relation plays a fundamental role for generation of reducts and threshold functions using the Boolean reasoning on the discernibility and in discernibility matrices, in which the indiscernibility matrix is proposed here to test the sufficient condition for reduct and threshold function. Finally, reduct-threshold network is proposed for the higher classification accuracy.
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Ishii, N., Torii, I., Iwata, K., Odagiri, K., Nakashima, T. (2017). Generation of Reducts and Threshold Functions and Its Networks for Classification. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_45
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DOI: https://doi.org/10.1007/978-3-319-68935-7_45
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