Communications of the ACM - Special issue on parallelism
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
The Knowledge Engineering Review
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The classification problem is one of the typical problems encountered in data mining and machine learning. In this paper, a rough genetic algorithm (RGA) is applied to the classification problem in an undetermined environment based on a fuzzy distance function by calculating attribute weights. The RGA, a genetic algorithm based on rough values, can complement the existing tools developed in rough computing. Computational experiments are conducted on benchmark problems downloaded from UCI machine learning databases. Experimental results, compared with the usual GA [1] and C4.5 algorithms, verify the efficiency of the developed algorithm. Furthermore, the weights acquired by the proposed learning method are applicable not only to fuzzy similarity functions but also to any similarity functions. As an application, a new distance metric called weighted discretized value difference metric (WDVDM) is proposed. Experimental results show that WDVDM is an improvement on the discretized value difference metric (DVDM).