Optimized selection of training samples for One-Class Neural Network classifier
Bilal Hadjadji, Youcef Chibani · 2014
One-Class Classification (OCC) based on the Auto-Associative Neural Networks (AANN) has been widely used in various recognition applications for its effective robustness. Its main advantage lies in the description of samples more accurately to other OCCs. However, it is considerably sensitive to the presence of outliers or noisy data contained into the training set, which may affect badly the representative model. Hence, we propose in this paper an algorithm that uses the AANN for selecting the most representative training samples. The same AANN is retrained to reproduce the selected samples for generating an optimal representative model. The experimental evaluation conducted on several real-world benchmarks confirms the effective use of the Selected Training Samples for Associative Neural Network (STS-AANN) versus the training on the entire set.