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Improved one-class classification using filled function
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DOI：10.1007/s10489-018-1145-y.png)
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摘要
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Novelty detection is the identification of new observation that a machine learning system is not aware. Detecting novel instances is one of the interesting topics in recent studies. The problem of the current methods is their high run-time, so often make them unusable for large data sets. This paper presents the proposed method concerning this problem. Focusing on the task of one-class classification, the labeled data are mapped into two hypersphere regions for target and non-target objects. This mapping process is considered as a nonlinear programming. The problem is solved by employing the filled function for finding global minimizer. The global minimizer is considered as a boundary which is fit the target class. In the end, a one-class classifier to detect target class members is obtained. To present the power of the proposed method, several experiments have been conducted based on 10-fold cross-validation over real-world data sets from UCI repository. Experimental results show that the proposed method is superior than the state-of-the-art competing methods regarding applied evaluation metrics.
Keyword:
Novelty detection
One-class classification
Optimization problem
Filled function
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