An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification

Younes, Zoulficar; Abdallah, Fahed; Denœux, Thierry

doi:10.1007/978-3-642-04388-8_23

Cited by 24 publications

(19 citation statements)

References 11 publications

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“…In Denoeux et al (2010), Younes et al (2009), the above framework was applied to multilabel classification (Zhang and Zhou 2007;Trohidis et al 2008). In this learning task, each object may belong simultaneously to several classes, contrary to standard single-label problems where objects belong to only one class.…”

Section: Application To Multi-label Classificationmentioning

confidence: 99%

“…For decision making, it was proposed in Denoeux et al (2010), Younes et al (2009) to use the following rule. Let Y be the predicted label set for instance x.…”

Section: Application To Multi-label Classificationmentioning

confidence: 99%

“…8 will conclude this paper. Note that the applications to multi-label classification and ensemble clustering have been described separately in Denoeux et al (2010), Younes et al (2009) and Denoeux (2009, 2011), respectively. The present paper presents the approach in much greater generality and provides a unified view of the two previous applications.…”

Section: Introductionmentioning

confidence: 99%

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Evidential reasoning in large partially ordered sets

Denœux

Masson

2011

Ann Oper Res

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The Dempster-Shafer theory of belief functions has proved to be a powerful formalism for uncertain reasoning. However, belief functions on a finite frame of discernment are usually defined in the power set 2 , resulting in exponential complexity of the operations involved in this framework, such as combination rules. When is linearly ordered, a usual trick is to work only with intervals, which drastically reduces the complexity of calculations. In this paper, we show that this trick can be extrapolated to frames endowed with an arbitrary lattice structure, not necessarily a linear order. This principle makes it possible to apply the Dempster-Shafer framework to very large frames such as the power set, the set of partitions, or the set of preorders of a finite set. Applications to multi-label classification, ensemble clustering and preference aggregation are demonstrated.

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Section: Application To Multi-label Classificationmentioning

confidence: 99%

“…For decision making, it was proposed in Denoeux et al (2010), Younes et al (2009) to use the following rule. Let Y be the predicted label set for instance x.…”

Section: Application To Multi-label Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidential reasoning in large partially ordered sets

Denœux

Masson

2011

Ann Oper Res

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“…The author defined a cost function and a special multi-label margin and then proposed an algorithm named Rank-SVM based on a ranking system combined with a label set size predictor. In [15], an evidence-theoretic k-NN rule for multi-label classification has been presented. This rule is based on an evidential formalism for representing uncertainties on the classification of multi-labelled data and handling imprecise labels, which has been detailed in [2].…”

Section: Introductionmentioning

confidence: 99%

“…A first group contains the indirect methods that transform a multi-label classification problem into binary classification problems (a binary classifier for each class or pairwise classifiers) [14] [1] or into multi-class classification problem (each subset of classes is considered as a new class) [9]. A second group consists in extending common learning algorithms and making them able to manipulate multi-label data directly [15].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy multi-label learning under veristic variables

Younes

Abdallah

Denoux

2010

International Conference on Fuzzy Systems

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Multi-label learning is increasingly required by many applications where instances may belong to several classes at the same time. In this paper, we propose a fuzzy k-nearest neighbor method for multi-label classification using the veristic variable framework. Veristic variables are variables that can assume simultaneously multiple values with different degrees. In multi-label learning, class labels can be considered as veristic variables since each instance can belong simultaneously to more than one class. Several applications on benchmark datasets demonstrate the efficiency of our approach.

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Multi‐label learning: a review of the state of the art and ongoing research

Gibaja

Ventura

2014

WIREs Data Min & Knowl

159

122

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Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up-to-date overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.

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An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification

Cited by 24 publications

References 11 publications

Evidential reasoning in large partially ordered sets

Evidential reasoning in large partially ordered sets

Fuzzy multi-label learning under veristic variables

Multi‐label learning: a review of the state of the art and ongoing research

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