Solving for multi-class using orthogonal coding matrices

01/27/2018

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by   Peter Mills, et al.

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Probability estimates are desirable in statistical classification both for gauging the accuracy of a classification result and for calibration. Here we describe a method of solving for the conditional probabilities in multi-class classification using orthogonal error correcting codes. The method is tested on six different datasets using support vector machines and compares favorably with an existing technique based on the one-versus-one multi-class method. Probabilities are validated based on the cumulative sum of a boolean evaluation of the correctness of the class label divided by the estimated probability. Probability estimation using orthogonal coding is simple and efficient and has the potential for faster classification results than the one-versus-one method.