A class of single-class minimax probability machines for novelty detection.
Single-class minimax probability machines (MPMs) offer robust novelty detection with distribution-free worst case bounds on the probability that a pattern will fall inside the normal region. However, in practice, they are too cautious in labeling patterns as outlying and so have a high false negative rate (FNR). In this paper, we propose a more aggressive version of the single-class MPM that bounds the best case probability that a pattern will fall inside the normal region. These two MPMs can then be used together to delimit the solution space. By using the hyperplane lying in the middle of this pair of MPMs, a better compromise between false positives (FPs) and false negatives (FNs), and between recall and precision can be obtained. Experiments on the real-world data sets show encouraging results.
Kwok
James T
JT
Institute of Systems Science, the Polish Academy of Sciences, Warsaw, Poland. jamesk@cs.ust.hk
Tsang
Ivor Wai-hung
IW
Zurada
Jacek M
JM
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003405:Creativity; D003661:Decision Support Techniques; D005716:Game Theory; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*