Abstract
In this paper we propose a novel method for one-class classification. The proposed method analyses the limit of all feature dimensions to find the true border which describes the normal class. To this end, it simulates the novelty class by creating artificial prototypes outside the normal description. The parameters involved in the definition of the border are optimized via particle swarm optimization (PSO), which enables the method to describe data distributions with complex shapes. An experimental analysis is conducted with the proposed method using twelve data sets and considering the performance measures (i) Area Under the ROC Curve (AUC), (ii) training time, and (iii) prototype reduction. A comparison with One-Class SVM (OCSVM), kMeansDD, ParzenDD and kNNDD is carried out. The results show that performance of the proposed method is equivalent to OCSVM regarding the AUC, yet the proposed method outperforms OCSVM regarding the number of stored prototypes and training time.
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Cabral, G.G., Oliveira, A.L.I. (2012). One-Class Classification through Optimized Feature Boundaries Detection and Prototype Reduction. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_87
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DOI: https://doi.org/10.1007/978-3-642-33269-2_87
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