A new SVM model for classifying genetic data

We propose a new formulation of the Support Vector Machine (SVM) for classifying genetic data. It is based on the development of ideas from the method of total least squares, in which assumed error in measured data are incorporated in the model design. For genetic data the number of features is always far greater than the sample size. Consequently, in our method, we introduce Lagrange multipliers and solve for the dual variables. Instead of finding the minimum value of the Lagrangian function, we solve the nonlinear system of equations obtained from the Karush-Kuhn-Tucker conditions. We also implement complementarity constraints and incorporate weighting of the linear system by the inverse covariance matrix of the measured data. The proposed algorithm gives improved results and higher sensitivity for classifying a set of Alzheimer’s Disease Positron Emission Tomography images as compared with SVM. It is also more robust to noise than SVM.

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