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Random kLabelsets: An Ensemble Method for Multilabel Classification
"... Abstract. This paper proposes an ensemble method for multilabel classification. The RAndom klabELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a singlelabel classifier for the prediction of each element in the powerset of ..."
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Cited by 102 (7 self)
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Abstract. This paper proposes an ensemble method for multilabel classification. The RAndom klabELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a singlelabel classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using singlelabel classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. Experimental results on common multilabel domains involving protein, document and scene classification show that better performance can be achieved compared to popular multilabel classification approaches. 1
Mining multilabel data
 In Data Mining and Knowledge Discovery Handbook
, 2010
"... A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such d ..."
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Cited by 88 (9 self)
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A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multilabel.
An Introduction to Conditional Random Fields
 Foundations and Trends in Machine Learning
, 2012
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Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
"... In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label dependence into account. The goal of this paper is to elaborate on this postulate in a critical way. To this end, we forma ..."
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Cited by 57 (3 self)
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In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label dependence into account. The goal of this paper is to elaborate on this postulate in a critical way. To this end, we formalize and analyze MLC within a probabilistic setting. Thus, it becomes possible to look at the problem from the point of view of risk minimization and Bayes optimal prediction. Moreover, inspired by our probabilistic setting, we propose a new method for MLC that generalizes and outperforms another approach, called classifier chains, that was recently introduced in the literature. 1.
Semisupervised Multilabel Learning by Constrained Nonnegative Matrix Factorization
, 2006
"... We present a novel framework for multilabel learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key assumption behind this work is that two examples tend to have large overlap in their assigned class memberships if they sh ..."
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Cited by 55 (1 self)
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We present a novel framework for multilabel learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key assumption behind this work is that two examples tend to have large overlap in their assigned class memberships if they share high similarity in their input patterns. We capitalize this assumption by first computing two sets of similarities, one based on the input patterns of examples, and the other based on the class memberships of the examples. We then search for the optimal assignment of class memberships to the unlabeled data that minimizes the difference between these two sets of similarities. The optimization problem is formulated as a constrained Nonnegative Matrix Factorization (NMF) problem, and an algorithm is presented to efficiently find the solution. Compared to the existing approaches for multilabel learning, the proposed approach is advantageous in that it is able to explore both the unlabeled data and the correlation among different classes simultaneously. Experiments with text categorization show that our approach performs significantly better than several stateoftheart classification techniques when the number of classes is large and the size of training data is small.
Combining InstanceBased Learning and Logistic Regression for Multilabel Classification
"... Abstract. Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classification, instancebased learning algorithms relying on the nearest neighbor estimation p ..."
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Cited by 54 (6 self)
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Abstract. Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classification, instancebased learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a framework that unifies instancebased learning and logistic regression, comprising both methods as special cases. This approach allows one to capture interdependencies between labels and, moreover, to combine modelbased and similaritybased inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction. 1
Extracting Shared Subspace for Multilabel Classification
"... Multilabel problems arise in various domains such as multitopic document categorization and protein function prediction. One natural way to deal with such problems is to construct a binary classifier for each label, resulting in a set of independent binary classification problems. Since the multipl ..."
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Cited by 54 (2 self)
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Multilabel problems arise in various domains such as multitopic document categorization and protein function prediction. One natural way to deal with such problems is to construct a binary classifier for each label, resulting in a set of independent binary classification problems. Since the multiple labels share the same input space, and the semantics conveyed by different labels are usually correlated, it is essential to exploit the correlation information contained in different labels. In this paper, we consider a general framework for extracting shared structures in multilabel classification. In this framework, a common subspace is assumed to be shared among multiple labels. We show that the optimal solution to the proposed formulation can be obtained by solving a generalized eigenvalue problem, though the problem is nonconvex. For highdimensional problems, direct computation of the solution is expensive, and we develop an efficient algorithm for this case. One appealing feature of the proposed framework is that it includes several wellknown algorithms as special cases, thus elucidating their intrinsic relationships. We have conducted extensive experiments on eleven multitopic web page categorization tasks, and results demonstrate the effectiveness of the proposed formulation in comparison with several representative algorithms.
Semisupervised Multilabel Learning by Solving a Sylvester Equation
"... Multilabel learning refers to the problems where an instance can be assigned to more than one category. In this paper, we present a novel Semisupervised algorithm for Multilabel learning by solving a Sylvester Equation (SMSE). Two graphs are first constructed on instance level and category level ..."
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Cited by 45 (0 self)
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Multilabel learning refers to the problems where an instance can be assigned to more than one category. In this paper, we present a novel Semisupervised algorithm for Multilabel learning by solving a Sylvester Equation (SMSE). Two graphs are first constructed on instance level and category level respectively. For instance level, a graph is defined based on both labeled and unlabeled instances, where each node represents one instance and each edge weight reflects the similarity between corresponding pairwise instances. Similarly, for category level, a graph is also built based on
Multiinstance multilabel learning
 Artificial Intelligence
"... In this paper, we propose the MIML (MultiInstance MultiLabel learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicate ..."
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Cited by 38 (16 self)
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In this paper, we propose the MIML (MultiInstance MultiLabel learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Consideringthat the degeneration process may lose information, we propose the DMimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming singleinstances into the MIML representation for learning, while SubCod works by transforming singlelabel examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the singleinstances or singlelabel examples directly.
Multilabel dimensionality reduction via dependence maximization
 In Proceedings of AAAI Conference on Artificial Intelligence(AAAI
, 2008
"... Multilabel learning deals with data associated with multiple labels simultaneously. Like other machine learning and data mining tasks, multilabel learning also suffers from the curse of dimensionality. Although dimensionality reduction has been studied for many years, multilabel dimensionality r ..."
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Cited by 37 (6 self)
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Multilabel learning deals with data associated with multiple labels simultaneously. Like other machine learning and data mining tasks, multilabel learning also suffers from the curse of dimensionality. Although dimensionality reduction has been studied for many years, multilabel dimensionality reduction remains almost untouched. In this paper, we propose a multilabel dimensionality reduction method, MDDM, which attempts to project the original data into a lowerdimensional feature space maximizing the dependence between the original feature description and the associated class labels. Based on the HilbertSchmidt Independence Criterion, we derive a closedform solution which enables the dimensionality reduction process to be efficient. Experiments validate the performance of MDDM.