Multiobjective Evolutionary Algorithm for Rule Extraction in Data Mining
Date Issued
2010
Date
2010
Author(s)
Chan, Yung-Hsiang
Abstract
In this thesis, the problem of rule extraction in data mining including numeric association rule mining and classification rule mining is addressed. Both tasks involve many objectives to be optimized simultaneously, where the objectives frequently contradict with each other. Two Pareto-based multiobjective evolutionary algorithms are proposed to solve these problems. By incorporating the concept of MOEA/D, the mating restriction and environmental selection enhance the exploitation and exportation ability through setting the uniform weight vectors. And the solution of subproblem defined in MOEA/D is modified to a set of solutions to obtain solutions with same fitness. For numerical association rule mining, the proposed algorithm follows the common framework to obtain frequent itemsets. For classification, a two-phase multiobjective evolutionary algorithm is proposed which combines both Michigan and Pittsburgh approach to find Pareto-optimal rules first and then to form the Pareto-optimal rule set. The policy for each rule set is different according to its preference when conflict between rules occurred. Through experiments upon synthetic datasets, the proposed algorithm for numeric association rule mining shows its correctness and efficiency. The proposed algorithm is also applied upon several public real life datasets for future comparison. And for classification, the experimental results show it’s competitive against existing rule-based and non-rule based classifiers upon several public datasets.
Subjects
Multiobjective Evolutionary Algorithm
Data Mining
Numeric Association Rule Mining
Classification Rule Mining
Type
thesis
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