Name
Title | ||
|---|---|---|
Optimizing Cost-Sensitive SVM for Imbalanced Data : Connecting Cluster to Classification. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Learning | Data mining,Data set,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics),Probability density function,Machine learning,Similarity matrix |
DocType | Volume | Citations |
Journal | abs/1702.01504 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiuyan Yan | 1 | 0 | 0.68 |
| Shixiong Xia | 2 | 102 | 13.28 |
| Fan-Rong Meng | 3 | 0 | 0.34 |