One-Class classification through optimized feature boundaries detection and prototype reduction

Authors:
George G. Cabral;Adriano L. I. Oliveira
Affiliations:
Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil;Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 4
Cited 0

Modified support vector novelty detector using training data with outliers

Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Letters: Novelty detection with constructive probabilistic neural networks

Neurocomputing
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.