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Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?
[article]
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
arXiv:1702.04013v1
fatcat:uabogeg74jbijhzl3g2lw3r3ey