Harnessing the Power of GPUs to Speed Up Feature Selection for Outlier Detection

作     者：Fatemeh Azmandian Member, IEEE, Ayse Yilmazer Student Member, IEEE, Jennifer G. Dy Member, IEEE Javed A. Aslam IEEE, Jennifer G. Dy Member, ACM David R. Kaeli Fellow, IEEE, Member, ACM 

作者机构：Department of Electrical and Computer Engineering Northeastern University Boston 02115-5096 U.S.A. College of Computer and Information Science Northeastern University Boston 02115-5096 U.S.A. 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2014年第29卷第3期

页      面：408-422页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：Direct For Education and Human Resources Division Of Graduate Education Funding Source: National Science Foundation 

主　　题：feature selection outlier detection imbalanced data GPU acceleration 

摘      要：Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation criterion for filter-based feature selection which has an eye towards the final goal of outlier detection. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared with popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit （GPU） to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points.