Abstract
The attribute coding approach has been used in the Rough Set Theory (RST) based classification problems. The attribute coding defined ranges of the attribute values as multi-thresholds. If attribute values can be defined as appropriate values, the appropriate number of rules will be generated. The attribute coding for the RST based rule derivations significantly reduces unnecessary rules and simplifies the classification results. Therefore, how the appropriate attribute values can be defined will be very critical for rule derivations by using the RST. In this study, the authors intend to introduce the particle swarm optimization (PSO) algorithm to adjust the attribute setting scopes as an optimization problem to derive the most appropriate attribute values in a complex information system. Finally, the efficiency of the proposed method will be benchmarked with other algorithms by using the Fisher’s iris data set. Based on the benchmark results, the simpler rules can be generated and better classification performance can be achieved by using the PSO based attribute coding method.
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Chang, JR., Jheng, YH., Lo, CH., Chang, B. (2011). Attribute Coding for the Rough Set Theory Based Rule Simplications by Using the Particle Swarm Optimization Algorithm. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22194-1_39
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DOI: https://doi.org/10.1007/978-3-642-22194-1_39
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