2017/02/06 by Qiuyan Yan, Yan, Qiuyan, Shixiong Xia +3 · 7 citations
Computer Science · Engineering · Mathematics · #Anomaly Detection Techniques and Applications #Artificial intelligence #Benchmark (surveying) #Classifier (UML) #Computer science #Data mining #Data set #Electricity Theft Detection Techniques #Engineering #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine learning #Mathematical optimization #Mathematics #Metric (unit) #Pattern recognition (psychology) #Penalty method #Similarity (geometry) #Support vector machine #cs.LG
paper · pdf · doi:10.48550/arxiv.1702.01504
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/02/06 · openalex publication_date 2017/02/06 · arxiv updated 2017/02/07 · openalex created_date 2017/03/16 · openalex updated_date 2026/07/28
Class imbalance is one of the challenging problems for machine learning in many real-world applications, such as coal and gas burst accident monitoring: the burst premonition data is extreme smaller than the normal data, however, which is the highlight we truly focus on. Cost-sensitive adjustment approach is a typical algorithm-level method resisting the data set imbalance. For SVMs classifier, which is modified to incorporate varying penalty parameter(C) for each of considered groups of examples. However, the C value is determined empirically, or is calculated according to the evaluation metric, which need to be computed iteratively and time consuming. This paper presents a novel cost-sensitive SVM method whose penalty parameter C optimized on the basis of cluster probability density function(PDF) and the cluster PDF is estimated only according to similarity matrix and some predefined hyper-parameters. Experimental results on various standard benchmark data sets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used cost-sensitive techniques.