Combining Wavelets and Computational Intelligence Methods with Applications on Multi-class Classification Datasets

In this paper, we propose a novel algorithm for wavelet feature extraction as input to a supervised Multi-Class Classifier to improve classification performance. In particular, to select the best wavelets coefficient features, we first compute the energy-based variance distribution from wavelets coefficients at different subbands as well as the entropy-based fuzzy measures associated with the training instances. Once we get these entropy-based fuzzy measures associated with the different subsets of wavelets subbands, we apply the M

• Type: Journal
• Year: 2009
• Where: EUSFLAT
• Authors: Carlos Campos Bracho
• Keywords: EUSFLAT 2009, Fuzzy Logic, Multivariate Mutual Information, Wavelets, Wavelets Subbands