Anomaly Detection Through Comparison of Heterogeneous Machine Learning Classifiers vs KPCA

The anomaly detection is applicable to wide range of critical infrastructure elements due to frequent change in anomaly occurrences and make sure to avoid all threats identified in regular. In this perception, we have to identify the abnormal patterns in applications and to model them by using a new adorned machine learning classifiers. In this paper we are investigating the performance by comparison of heterogeneous machine learning classifiers: ICA (Independent Component Analysis), LDA (Linear Discriminant Analysis), PCA (Principal Component Analysis), Kernel PCA and other learning classifiers. The Kernel PCA (KPCA) is a non-linear extension to PCA used to classify the data and detect anomalies by orthogonal transformation of input space into (usually high dimensional) feature space. The KPCA use kernel trick by extract the principal components from set of corresponding eigenvectors and use kernel width as performance parameter to determine rate of classification. The KPCA is implemented on taking two UCI machine learning repository sets and one real bank dataset. The KPCA implemented with classic Gaussian kernel internally. Finally KPCA performance compared with projection methods (ICA, LDA, PLSDA and PCA), other kernel (SVM-K) and non-kernel techniques (ID3, C4.5, Rule C4.5, k-NN and NB) applied on same datasets using training and test set combinations.