A Multi-Objective Immune Clone Algorithm for Attribute Selection

H. Mo and L. Xu (PRC)


Attribute selection, immune clone algorithm,multi-objective optimization, data mining


The problem of feature selection in data mining is an important problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem, this paper proposes a Pareto immune clone algorithm(PICA) for feature selection based on the wrapper approach. The algorithm’s main goal is to find the best subset of features that minimizes both the error rate and the size of the tree discovered by C4.5, a classification algorithm, using the Pareto dominance concept.

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