Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers? [article]

Piotr Szymański, Tomasz Kajdanowicz
2017 arXiv   pre-print
We study the performance of data-driven, a priori and random approaches to label space partitioning for multi-label classification with a Gaussian Naive Bayes classifier. Experiments were performed on 12 benchmark data sets and evaluated on 5 established measures of classification quality: micro and macro averaged F1 score, Subset Accuracy and Hamming loss. Data-driven methods are significantly better than an average run of the random baseline. In case of F1 scores and Subset Accuracy - data
more » ... ven approaches were more likely to perform better than random approaches than otherwise in the worst case. There always exists a method that performs better than a priori methods in the worst case. The advantage of data-driven methods against a priori methods with a weak classifier is lesser than when tree classifiers are used.
arXiv:1702.04013v1 fatcat:uabogeg74jbijhzl3g2lw3r3ey