An innovative approach for feature selection based on chicken swarm optimization

Citation:
Hafez, A. I., Hossam M. Zawbaa, E. Emary, and A. E. H. Hamdi A. Mahmoud, "An innovative approach for feature selection based on chicken swarm optimization", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan,, November 13 - 1, 2015.

Date Presented:

November 13 - 1

Abstract:

In this paper, a system for feature selection based
on chicken swarm optimization (CSO) algorithm is proposed.
Datasets ordinarily includes a huge number of attributes, with
irrelevant and redundant attribute. Commonly wrapper-based
approaches are used for feature selection but it always requires
an intelligent search technique as part of the evaluation function.
Chicken swarm optimization (CSO)is a new bio-inspired
algorithm mimicking the hierarchal order of the chicken
swarm and the behaviors of chicken swarm, including roosters,
hens and chicks, CSO can efficiently extract the chickens’
swarm intelligence to optimize problems. Therefore, CSO was
employed to feature selection in wrapper mode to search
the feature space for optimal feature combination maximizing
classification performance, while minimizing the number of
selected features. The proposed system was benchmarked
on 18 datasets drawn from the UCI repository and using
different evaluation criteria and proves advance over particle
swarm optimization (PSO) and genetic algorithms (GA) that
commonly used in optimization problems