Abnormality prediction in high dimensional dataset among semi supervised learning approaches

Abnormality or inconsistency detection within a data is an attempt to make a distinction between usual and exceptional data instances. In this paper, we have proposed a novel methodAbnormality Prediction in High Dimensional Dataset among Semi Supervised Learning approaches (AP-HDD-SSL) to match the efficiencies of different semi supervised machine learning approaches using high dimensional KDD CUP 99 dataset. The pre-processing phase with dimensionality diminution is done prior to clustering using RFE (Random Forest Ensemble). Clustering with k-Means is initiated after the pre-processing phase for storing the most anomalous cluster. The classification within the cluster is done with semi-supervised learning approaches: k-Nearest Neighbour (k-NN), Linear Discriminant Analysis (LDA), Support Vector Machine-RFE(SVM-RFE), that are analysed and compared with the existing Over Sampling-PCA(os-PCA) method. The comparison results with Pima Indian and KDD cup 99 in terms of Accuracy, Detection Rate and AUC scores summarizes that AP-HDD-SSL with SVM-RFE outranked the other approaches.