2014
DOI: 10.1016/j.patrec.2014.07.005
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Interval prediction for graded multi-label classification

Abstract: Multi-label was introduced as an extension of multi-class classification. The aim is to predict a set of classes (called labels in this context) instead of a single one, namely the set of relevant labels. If membership to the set of relevant labels is defined to a certain degree, the learning task is called graded multi-label classification. These learning tasks can be seen as a set of ordinal classifications. Hence, recommender systems can be considered as multi-label classification tasks. In this paper, we p… Show more

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Cited by 6 publications
(4 citation statements)
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“…While these approaches outperformed the predictive model developed in [1], they can only model pairwise dependencies. Lastra et al [13] proposed a non-deterministic learner based on binary relevance that returns an interval whenever the classification is uncertain for a label. This method relies on a tradeoff between the size of the interval and the improvement of the accuracy.…”
Section: A Graded Multi-label Classifiersmentioning
confidence: 99%
“…While these approaches outperformed the predictive model developed in [1], they can only model pairwise dependencies. Lastra et al [13] proposed a non-deterministic learner based on binary relevance that returns an interval whenever the classification is uncertain for a label. This method relies on a tradeoff between the size of the interval and the improvement of the accuracy.…”
Section: A Graded Multi-label Classifiersmentioning
confidence: 99%
“…al. [18] introduced a nondeterministic learner based on a binary relevance strategy that returns a prediction interval whenever the classification is uncertain for a label. The authors claim that using narrow intervals for a label prediction greatly improves the classification accuracy.…”
Section: A Graded Multilabel Classificationmentioning
confidence: 99%
“…Recently, in [12], the graded multi-label setting was demonstrated to be a more fitting paradigm for Music Emotion Recognition than the standard single-label and multilabel approaches. Furthermore, GMLC was successfully deployed as a framework for recommendation systems [13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…Albeit its simplicity, BR completely ignores the underlying dependencies between the labels which lead to a loss in information and a decline in accuracy. Different transformationbased approaches were proposed with the aim of taking into account these interdependencies [13][14][15][18][19][20]. These methods relied on intuitive transformation schemas, thus decomposing the original problem into a number of multi-label classification tasks that are then solved using various approaches based on RPC (ranking by pairwise comparison), CLR (calibrated label ranking), IBLR (instance based logistic regression), etc.…”
Section: Introductionmentioning
confidence: 99%