Feature Selection in Pathology Detection using Hybrid Multidimensional Analysis

Heuristical algorithms can reduce the computational complexity. Such methods require of some stopping criteria (cost function). Some of these cost functions are based on statistics like univariate and multivariate methods of analysis. Dimensional reduction techniques such as principal component analysis (PCA) allow to find a lower dimension transformed space based on data variance, but this procedure does not take into account information about classes separability, the direction of maximum variance does not necessarily correspond to the direction of maximum separability. In this work, we propose a feature selection algorithm with heuristic search that uses multivariate analysis of variance (MANOVA) as the cost function. This technique is put to test by classifying hypernasal from normal voices of CLP (cleft lip and/or palate) patients. The classification performance, computational time and reduction ratio are also considered by the comparison with an alternate feature selection method founded on unfolding the multivariate analysis into univariate and bivariate analysis