On the Estimation of Predictive Evaluation Measure Baselines for Multi-label Learning

Abstract

Machine learning research relies to a large extent on experimental observations. The evaluation of classifiers is often carried out by empirical comparison with classifiers generated by different learning algorithms, allowing the identification of the best algorithm for the problem at hand. Nevertheless, previously to this evaluation, it is important to state if the classifiers have truly learned the domain class concepts, which can be done by comparing the classifiers’ predictive measures with the ones from the baseline classifiers. A baseline classifier is the one constructed by a naïve learning algorithm which only uses the class distribution of the dataset. However, finding naïve classifiers in multi-label learning is not as straightforward as in single-label learning. This work proposes a simple way to find baseline multi-label classifiers. Three specific and one general naïve multi-label classifiers are proposed to estimate the baseline values for multi-label predictive evaluation measures. Experimental results show the suitability of our proposal in revealing the learning power of multi-label learning algorithms.