A Novel Hybrid Grey Wolf Optimization Algorithm using Two-Phase Crossover Approach for Feature Selection and Classification

Mukesh Nimbiwal, Jyoti Vashishtha


Data mining process can be hampered by high dimensional large datasets, so feature selection become a mandatory task in prior for dimensionality reduction of datasets. Main motive of feature selection process is to choose most informative features and use them to maximize the classification accuracy. This work introduces a novel two phase crossover operator with grey wolf algorithm to solve the problem of feature selection. Two phase crossover improves the exploitation part. First phase crossover is used for feature selection and second phase used for adding some more important information and improve the classification accuracy. The KNN classifier improved the classification accuracy which is most famous classifier based on wrapper method. Ten-fold crossover validation is used to defeat the over-fitting problem which is always a milestone in the way of accuracy. Experiments are applied using various datasets and results prove that proposed algorithm outperform and provide better results.


ALO (Ant Lion algorithm), BGOA (binary grasshopper approach), FS (feature selection), GWO (Grey Wolf Optimization), KNN (K-Nearest neighbor), PSO (Particle Swarm Optimization), TCGWO (Two-Phase Crossover Grey Wolf Optimization)

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