B-bleaching: Agile Overtraining Avoidance in the WiSARD Weightless Neural Classifier

Weightless neural networks constitute a still not fully explored Machine Learning paradigm, even if its first model, WiSARD, is considered. Bleaching, an improvement on WiSARD’s learning mechanism was recently proposed in order to avoid overtraining. Although presenting very good results in different application domains, the original sequential bleaching and its confidence modulation mechanisms still offer room for improvement. This paper presents a new variation of the bleaching mechanism and compares the three strategies performance on a complex domain, that of multilingual grammatical categorization. Experiments considered both number of iterations and accuracy. Results show that binary bleaching allows for a considerable improvement to number of iterations whilst not introducing loss of accuracy.