![]() ![]() The AUC values for the three datasets are 0.726, 0.775, and 0.750 respectively. The AUC values based on K-NN with PCA enhanced for three datasets CM1, KC3, MC2. Based on the area under curve (AUC) performance measurement PCA feature selection and bagging based on K-NN perform better than both bagging based on SVM and boosting based on K-NN and SVM. Tentative results indicate that ensemble methods can improve the model's performance without the use of feature selection techniques. Five datasets, obtained from the PROMISE software depository, are analysed. ![]() The aim of this research is to assess the effectiveness of feature selection techniques using ensemble techniques. ![]() Four feature selection techniques are employed: Principal Component Analysis (PCA), Pear-son's correlation, Greedy Stepwise Forward selection, and Information Gain (IG). In this research we look at two potential scenarios: (1) Ensemble models constructed from the original datasets, without feature selection, and (2) Ensemble models constructed from the reduced datasets after feature selection has been applied. This research highlights a procedure which includes a feature selection technique to single out relevant attributes, and an ensemble technique to handle the class-imbalance issue. ![]()
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