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arXiv · 1702.04013

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

Piotr Szymański·Tomasz Kajdanowicz
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Abstract

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 driven 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.

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Piotr Szymański, Tomasz Kajdanowicz. 2017-02-13. Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?. https://arxiv.org/abs/1702.04013

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