On multiclass classification through the minimization of the confusion matrix norm

5th Asian Conference on Machine Learning

数学科学;计算机科学

Sokol Koc¸o [e-mail]

PID  :  123094

来源: CEUR

【 摘 要 】

In imbalanced multiclass classification problems, the misclassification rate as an er ror measure may not be a relevant choice. Several methods have been developed where the performance measure retained richer information than the mere misclassification rate: misclassification costs, ROCbased information, etc. Following this idea of dealing with al ternate measures of performance, we propose to address imbalanced classification problems by using a new measure to be optimized: the norm of the confusion matrix. Indeed, recent results show that using the norm of the confusion matrix as an error measure can be quite interesting due to the finegrain informations contained in the matrix, especially in the case of imbalanced classes. Our first contribution then consists in showing that optimizing criterion based on the confusion matrix gives rise to a common background for costsensitive methods aimed at dealing with imbalanced classes learning problems. As our second contribution, we propose an extension of a recent multiclass boosting method — namely AdaBoost.MM — to the imbalanced class problem, by greedily minimizing the empirical norm of the confusion matrix. A theoretical analysis of the properties of the proposed method is presented, while experimental results illustrate the behavior of the algorithm and show the relevancy of the approach compared to other methods.