A Novel Hybrid Feature Selection Technique for Data Mining Applications

One of the most challenging issues these days is managing massive amounts of data that must be examined. In data mining applications, feature selection is extremely crucial. Feature Selection picks the fewest characteristics from many features requiring more calculation time, vast space, etc. Feature selection has captivated the interest of many researchers working on machine learning and data mining since it allows classifiers to be faster, more cost-effective, and more accurate. The previous study proposes a particle swarm optimization (PSO) approach with a few drawbacks. It can simply move into a local optimum and include minimum convergence ratio. However, when used to handle high-dimensional and complex problems, PSO’s computational complexity is acceptable. To address the issue to choose a subsection of characteristics with minimal redundancy &maximal relevance to classification, this study proposes a hybrid IPSO with a K-means technique. Initially, to normalize data, Z-score method is used. To enhance the accuracy of classifier, hybrid attribute extraction strategy is designed. Finally, the Support Vector Machine-based classifier is used to rank feature selection approaches based on their classification accuracy for a specific dataset. In this case, two datasets are used: WDBC and Hepatitis. According to the simulation findings, the suggested approach yields better efficiency than the traditional technologies.