Bias Estimation and Correction in a Classifier using Product of Likelihood-Gaussians
T. Nagarajan, Douglas D. O’Shaughnessy · 2007
In any classification task the confusion error, in general, is proportional to the number of classes. This is mainly due to sharing of some common attributes (feature vectors) among different classes. This, in many cases, leads to a serious problem, in the sense that, the classifier itself may be biased towards a specific class or a subset of classes. An ideal classifier is not expected to have any such bias. If we assume that, for a given pair of models and their corresponding training data, the log-likelihoods are distributed normally, the bias of any of these models may be visualized in the likelihood-space as an overlap between Gaussian likelihoods of different models (classes). In this paper, we propose a discriminant measure, using a product of Gaussian likelihoods, to estimate the amount of bias. By adjusting the complexity of the models, we show that this bias can be neutralized and a better classification accuracy can be achieved. Presently, the experiments are carried out on the OGLMLTS telephone speech corpus on a language identification task. The results show that a better classification accuracy can be achieved without any degradation in the performance of any of the individual classes.