Feature Reduction in Classification Tasks using Bio-inspired Optimization Algorithms

Abstract

In big data, there is a major difficulty that requires data mining to be conducted with elevated data in big technology, which would be gaining a lot of traction nowadays. When it comes to Big Data, feature selection approaches are seen to be a game changer since they can assist minimize the complexity of data, making it simpler to study and translate it into meaningful information. To enhance classification performance, feature selection removes unnecessary and redundant characteristics from the dataset. In this paper, Grey Wolf Approaches based on Quantum leaping neighbor memeplexes termed as QLGWONM is proposed. The result shows that when compared to the some bio-inspired algorithms such as PSO, GWO, ABA, CSA models, the suggested model performed well in terms of accuracy and have accuracy of 100% for Brain Tumor, CNS, Lung dataset and 97.1% for Ionosphere dataset and 99% for NSL-KDD.