Evidence generation for Dempster-Shafer fusion using feature extraction multiplicity and radial basis network

Feature extraction methods in pattern recognition tasks seek to transform data variables to abstract mathematical variables such that their scores (called features) reveal hidden data structure of high cognitive value. Various feature extraction methods process raw data from different perspectives. Some depend on statistical correlation or independence such as principal component analysis (PCA), independent component analysis (ICA), singular value decomposition (SVD) and linear discriminant analysis (LDA), and some others aim to model parametric representations such as partial-least-square regression (PLSR). These methods can be viewed as independent observers who generate different feature sets for describing the same data. In supervised pattern recognition problems, this viewpoint can be combined with a classifier function to generate independent sets of class likelihood. The latter can be interpreted as evidences for class identities assigned by independent expert systems consisted of feature extraction method and classifier function combinations. Having created such set of experts, one can employ an information fusion system that could predict class identities. Following this paradigm, we used above mentioned feature extraction methods paired with a radial basis network to generate evidences, and applied Dempster-Shafer (D-S) fusion for pattern classification in a number of benchmark data sets. It is found that DS fusion results in enhanced classification rates compared to results from individual expert systems.