An Entropy-based Adaptive Genetic Algorithm Approach on Data Mining for Classification Problems

Provided by: International Journal of Computing Science and Information Technology (IJCSIT)
Topic: Big Data
Format: PDF
Genetic algorithm is one of the commonly used approaches on data mining. In this paper, the authors put forward a genetic algorithm approach for classification problems. Binary coding is adopted in which an individual in a population consists of a fixed number of rules that stand for a solution candidate. The evaluation function considers four important factors which are error rate, entropy measure, rule consistency and hole ratio, respectively. Adaptive asymmetric mutation is applied by the self-adaptation of mutation inversion probability from 1-0 (0-1). The generated rules are not disjoint but can overlap.

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