CLASSIFIER COMBINATION IN SPEECH RECOGNITION

In statistical pattern recognition, the principal task is to classify abstract data sets. Instead of using robust but computational expensive algorithms it is possible to combine `weak´ classifiers that can be employed in solving complex classification tasks. In this comparative study, we will examine the effectiveness of the commonly used hybrid schemes - especially those used for speech recognition problems - concentrating on cases which employ different combinations of classifiers

An empirical evaluation of imbalanced data strategies from a practitioner's point of view

This research tested the following well known strategies to deal with binary imbalanced data on 82 different real life data sets (sampled to imbalance rates of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline (just the base classifier). As base classifiers we used SVM with RBF kernel, random forests, and gradient boosting machines and we measured the quality of the resulting classifier using 6 different metrics (Area under the curve, Accuracy, F-measure, G-mean, Matthew's correlation coefficient and Balanced accuracy). The best strategy strongly depends on the metric used to measure the quality of the classifier. For AUC and accuracy class weight and the baseline perform better; for F-measure and MCC, SMOTE performs better; and for G-mean and balanced accuracy, underbagging