Publication Type : Conference Paper
Publisher : Symposium on Emerging Topics in Computing and Communications (SETCAC’16), International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Source : Symposium on Emerging Topics in Computing and Communications (SETCAC’16), International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016
Url : https://ieeexplore.ieee.org/abstract/document/7732226
Keywords : angle based outlier detection, Anomaly detection, anomaly detection phase, attribute reduction based anomaly detection, Clustering, Clustering algorithms, clustering dependent oversampling PCA, Computer science, Covariance matrices, data behavior, data instances differentiation, Data mining, decremental PCA, Eigen vector, Informatics, k-median, k-median clustering approach, local outlier factor, measurement, median based outlier detection, naive oversampling PCA, online oversampling PCA, os-PCA methodology, Over-Principal Component Analysis, pattern clustering, preprocessing phase, Principal component analysis, random forest ensemble method, recursive feature elimination method, RF-ensemble method, RFE method
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : No
Year : 2016
Abstract : Anomaly detection refers to the task of estimating and finding patterns which do not comply with the general behavior of data. Moreover, a range of assumptions are made so as to differentiate between normal and deviated data instances. This paper describes a solution approach to this problem using a two-step phase including an important preprocessing phase and anomaly detection phase. For the preprocessing phase, we have used two methods mainly: Recursive Feature Elimination method (RFE) and Random Forest Ensemble (RF-Ensemble) method. For the next phase of anomaly detection, we have used Clustering based Oversampling PCA (os-PCA) methodology. The k-median clustering approach is utilized for this purpose. The technique was implemented and tested on various standard data sets like Pima, Splice etc. The results were also compared with the existing state of the methods in this field like online Oversampling PCA, Naive Oversampling PCA, decremental PCA, Local Outlier Factor, Angle Based Outlier detection and Median Based Outlier Detection approaches. The testing results confirm that the proposed approach outperformed all other methods on the basis of accuracy, AUC scores etc.
Cite this Research Publication :
Asha Ashok, Smitha, S., and Krishna, M. H. K., “Attribute reduction based anomaly detection scheme by clustering dependent oversampling PCA”, in Symposium on Emerging Topics in Computing and Communications (SETCAC’16), International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016