An ensemble approach for cancerious dataset analysis using feature selection

Feature selection (FS) is an important technique in data mining to remove noise, irrelevant and redundant data. The paper introduces the ensemble approach using FS and without using FS tested on a standard medical dataset in order to compare the accuracy and time of both. This system uses best first search FS algorithm to reduce the noise in the dataset. The ensemble technique is a combination of two or more classifiers i.e. meta classifiers and classifiers. Bagging, Boosting and Adaboost are meta classifiers. In the proposed work Bagging and Adaboost ensembles are used, but the main focus is on Bagging Ensembles as it has been proven best compared to Adaboost and Boosting ensembles [1]. This paper concludes that better results can be achieved by applying FS on ensembles.