A Granular Computing Approach to Symbolic Value Partitioning

Abstract

Symbolic value partitioning is a knowledge reduction technique in the field of data min- ing. In this paper, we propose a granular computing approach for the partitioning task that includes granule construction and granule selection algorithms. The granule construction algorithm takes advantage of local information associated with each attribute. A binary attribute value taxonomy tree is built to merge these attribute values in a bottom-up manner using information-loss heuristics. The use of a balancing technique enables us to control different nodes in the same level to have approximately the same size. The granule selection algorithm uses global information about all of the attributes in the decision system. Hence, nodes across the taxonomy forest of all attributes are selected and expanded using information-gain heuristics. We present a series of experimental results that demonstrate the effectiveness of the proposed approach in terms of reducing the data size and improving the resulting classification accuracy.