id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_2pw4vkdn4jh4dohqkeifcbjleq	Cláudio Rebelo de Sá	Label Ranking Forests	2016	21	.pdf	application/pdf	6538	607	71	developed/adapted to treat rankings of a fixed set of labels as the target object, include several different types of decision trees (DT). Label Ranking (LR) is an increasingly popular topic in the machine learning literature (Ribeiro et al., 2012; de Sá et al., 2011; Cheng & Hüllermeier, Tree-based models have been used in classification (Quinlan, 1986), regression (Breiman et al., 1984) and also label ranking (Todorovski et al., 2002; This paper extends previous work (de Sá et al., 2015), in which we proposed a new version of decision trees for LR, called the Entropy-based Ranking Trees and empirically compared them to existing approaches. In this section, we start by formalizing the problem of label ranking (Section 2.1) and then we discuss the adaptation of the decision trees algorithm Tree-based models have been used in classification (Quinlan, 1986), regression (Breiman et al., 1984), and label ranking (Todorovski et al., 2002; Cheng	./cache/work_2pw4vkdn4jh4dohqkeifcbjleq.pdf	./txt/work_2pw4vkdn4jh4dohqkeifcbjleq.txt