Affiliations: [a] Systems Engineering, Department of Electrical Engineering, IIT(BHU) Varanasi, India
| [b] Department of Electrical Engineering, IIT(BHU) Varanasi, India
Correspondence:
[*]
Corresponding author. Tarun Maini, Systems Engineering, Department of Electrical Engineering, IIT(BHU)
Varanasi, India. E-mail: [email protected].
Abstract: This paper focuses on Fuzzy rough set, which is the fusion of fuzzy sets and rough sets theory for doing feature selection. For selecting the appropriate feature subset, swarm algorithms are used. The fitness function used here is Fuzzy Rough Dependency Measure. This paper demonstrates that by optimizing the fitness function, swarm algorithms are capable to select the best subset of features. Further, in this paper, an attempt has been made to improve the capability of the swarm based algorithms such as Intelligent Dynamic Swarm (IDS) and Particle Swarm Optimization (PSO) through modified initialization of solutions, for picking the appropriate features for the feature selection task. Improvement in the size of reducts and classification accuracy of these reducts are observed when initialization is done using the proposed method. Statistical t-tests have also been performed for the validation of the results.
