Comparison of Subset Evaluation Feature Selection Algorithm using KDD Cup’99 Dataset for Mobile Ad-hoc Network
Now a days many researches have been carried out on the feature selection process. This feature selection process is more important for any research, since the research will be using any one of the available dataset. These dataset will have many features and incomputable instances. The data in a dataset is not necessary to be more suited for any research process. The features selection algorithms are extract the suitable features for a particular research from the whole dataset. This article provides such feature selection algorithms which are working based on the correlation and consistency. Also the correlation and consistency based algorithms are applied on the standard dataset called KDD Cup’99 and by which these algorithms are compared with each other. The results are analyzed and the performance of the feature selection algorithms concluded. This conclusion could be used for any intrusion detection system on a mobile ad-hoc networks.