Comparison of score normalization methods applied to multi-label classification

Our paper deals with the multi-label text classification of the newspaper articles, where the classifier must decide if a document does or does not belong to each topic from the predefined topic set. A generative classifier is used to tackle this task and the problem with finding a threshold for the positive classification is mainly addressed. This threshold can vary for each document depending on the content of the document (words used, length of the document, etc.). An extensive comparison of the score normalization methods, primary proposed in the speaker identification/verification task, for robustly finding the threshold defining the boundary between the “correct” and the “incorrect” topics of a document is presented. Score normalization methods (based on World Model and Unconstrained Cohort Normalization) applied to the topic identification task has shown an improvement of results in our former experiments, therefore in this paper an in-depth experiments with more score normalization techniques applied to the multi-label classification were performed. Thorough analysis of the effects of the various parameters setting is presented.