Abstract
We propose a method for estimating class membership probabilities of a predictedclass in multiclass classification, using scores outputted by a classifier (classification scores), not only for the predicted class but also for other classes in a document classification.Class membership probabilities are important in many applications of document classification, in which multiclass classification is often applied.As a ethod for estimating class membership probabilities by using multiple scores, we propose two kinds of methods.One is generating an accuracy table with smoothing methods such as the moving average or a moving average with coverage, which indirectly estimates class membership probabilities by referring the accuracy table. The other is applying a logistic regression estimated parameters beforehand, which directly estimate these probabilities.Through experiments on two different datasets with both Support Vector Machines and Naive Bayes classifiers, we show that the use of multiple classification scores is much effective in both methods.We also show that the proposed smoothing method for the accuracy table works quite well, and that the method applying a logistic regression is more stable.Moreover, the estimated class membership probabilities by the proposed method are useful in the detection of the misclassified samples.
