Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper we study the applicability of a clustering validation measure, the Adjusted Rand Index (ARI), for this task comparing it with other methods based on statistical tests and on ROC curve. We have performed some experiments that show the validity of the proposed method.

In a previous paper (1987), the authors proposed a parallel computational scheme that is based on the mathematical decomposition of the equations into their primitive matrix/vector arithmetic operations. It was shown that the mathematical decomposition scheme provides an efficient mechanism to reduce the computational cycle of both the Newton-Euler (N-E) and the Lagrange-Euler (L-E) formulations. In the present paper, the N-E and L-E equations are analyzed from a hardware perspective and the results for each are compared. The analysis shows that N-E is more efficient than L-E from the computational as well as the hardware point of view.>