Soft decision trees: A genetically optimized cluster oriented approach
When descriptions of data values are too detailed, the computational complexities involved in mining useful knowledge from the database generally increases. This gives rise to the need of tools techniques which can reduce these complexities and mine the valuable information hidden behind the database. There exists number of such techniques viz. decision trees, neural networks, rough-set theory, rule induction, and case-based reasoning which are able to meet the aforesaid objective up to some extent. Each of these techniques has its advantages and limitations that motivate researchers to develop new tools for the mining tasks. In this paper, we have developed a novel methodology, genetically optimized cluster oriented soft decision trees (GCSDT), to glean vital information imbedded in the large databases. In contrast to the standard C-fuzzy decision trees, where granules are developed through fuzzy (soft) clustering, in the proposed architecture granules are developed by means of genetically optimized soft clustering. In the GCSDT architecture, GA ameliorates the difficulty of choosing an initialization for the fuzzy clustering algorithm and always avoids degenerate partitions. This provides an effective means for the optimization of clustering criterion, where an objective function can be illustrated in terms of clusters center. Growth of the GCSDT is realized by expanding nodes of the tree, characterized by the highest inconsistency index of the information granules. In order to validate the proposed tree structure it has been deployed on synthetic and machine learning data sets. Moreover, Results are compared with those produced by standard C4.5 decision trees and C-fuzzy decision trees; further student t-test is applied to show that these differences in results are statistically significant.
decision trees fuzzy clustering inconsistency index genetic algorithm