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Classifier Chains for Multilabel Classification
"... Abstract. The widely known binary relevance method for multilabel classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its labelindependence assumption. Instead, most current methods invest considerable ..."
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Cited by 162 (13 self)
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Abstract. The widely known binary relevance method for multilabel classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its labelindependence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevancebased methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multilabel datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and stateoftheart methods, both in terms of predictive performance and time complexity. 1
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 60 (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.
Multilabel learning by exploiting label dependency
 In KDD
, 2010
"... In multilabel learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multilabel examples is rather challenging. Therefor ..."
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Cited by 56 (2 self)
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In multilabel learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multilabel examples is rather challenging. Therefore, the key to successful multilabel learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the conditional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multilabel learning is decomposed into a series of singlelabel classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other wellestablished methods.
A Review on MultiLabel Learning Algorithms
"... Multilabel learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made towards this emerging machine learning paradigm. This paper aims to provide a ..."
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Cited by 41 (7 self)
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Multilabel learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made towards this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on stateoftheart multilabel learning algorithms. Firstly, fundamentals on multilabel learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multilabel learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multilabel learning are outlined for reference purposes.
Multilabel classification using conditional dependency networks
 in Proceedings of the 22nd International Joint Conference on Artificial Intelligence
, 2011
"... In this paper, we tackle the challenges of multilabel classification by developing a general conditional dependency network model. The proposed model is a cyclic directed graphical model, which provides an intuitive representation for the dependencies among multiple label variables, and a well in ..."
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Cited by 27 (5 self)
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In this paper, we tackle the challenges of multilabel classification by developing a general conditional dependency network model. The proposed model is a cyclic directed graphical model, which provides an intuitive representation for the dependencies among multiple label variables, and a well integrated framework for efficient model training using binary classifiers and label predictions using Gibbs sampling inference. Our experiments show the proposed conditional model can effectively exploit the label dependency to improve multilabel classification performance. 1
Multidimensional classification with Bayesian networks.
 International Journal of Approximate Reasoning,
, 2011
"... Multidimensional classification aims at finding a function that assigns a vector of class values to a given vector of features. In this paper, this problem is tackled by a general family of models, called multidimensional Bayesian network classifiers (MBCs). This probabilistic graphical model org ..."
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Cited by 24 (7 self)
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Multidimensional classification aims at finding a function that assigns a vector of class values to a given vector of features. In this paper, this problem is tackled by a general family of models, called multidimensional Bayesian network classifiers (MBCs). This probabilistic graphical model organizes class and feature variables as three different subgraphs: class subgraph, feature subgraph, and bridge (from class to features) subgraph. Under the standard 01 loss function, the most probable explanation (MPE) must be computed, for which we provide theoretical results in both general MBCs and in MBCs decomposable into maximal connected components. Moreover, when computing the MPE, the vector of class values is covered by following a special ordering (gray code). Under other loss functions defined in accordance with a decomposable structure, we derive theoretical results on how to minimize the expected loss. Besides these inference issues, the paper presents flexible algorithms for learning MBC structures from data based on filter, wrapper and hybrid approaches. The cardinality of the search space is also given. New performance evaluation metrics adapted from the singleclass setting are introduced. Experimental results with three benchmark data sets are encouraging, and they outperform stateoftheart algorithms for multilabel classification.
Multilabel Classification with Metalevel Features
"... Effective learning in multilabel classification (MLC) requires an appropriate level of abstraction for representing the relationship between each instance and multiple categories. Current MLC methods have been focused on learningtomap from instances to ranked lists of categories in a relatively h ..."
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Cited by 15 (2 self)
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Effective learning in multilabel classification (MLC) requires an appropriate level of abstraction for representing the relationship between each instance and multiple categories. Current MLC methods have been focused on learningtomap from instances to ranked lists of categories in a relatively highdimensional space. The finegrained features in such a space may not be sufficiently expressive for characterizing discriminative patterns, and worse, make the model complexity unnecessarily high. This paper proposes an alternative approach by transforming conventional representations of instances and categories into a relatively small set of linkbased metalevel features, and leveraging successful learningtorank retrieval algorithms (e.g., SVMMAP) over this reduced feature space. Controlled experiments on multiple benchmark datasets show strong empirical evidence for the strength of the proposed approach, as it significantly outperformed several stateoftheart methods, including RankSVM, MLkNN and
On label dependence in multilabel classification
 In Workshop Proceedings of Learning from MultiLabel Data, The 27th International Conference on Machine Learning
, 2010
"... The aim of this paper is to elaborate on the important issue of label dependence in multilabel classification (MLC). Looking at the problem from a statistical perspective, we claim that two different types of label dependence should be distinguished, namely conditional and unconditional. We formall ..."
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Cited by 13 (1 self)
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The aim of this paper is to elaborate on the important issue of label dependence in multilabel classification (MLC). Looking at the problem from a statistical perspective, we claim that two different types of label dependence should be distinguished, namely conditional and unconditional. We formally explain the differences and connections between both types of dependence and illustrate them by means of simple examples. Moreover, we given an overview of stateoftheart algorithms for MLC and categorize them according to the type of label dependence they seek to capture. 1.
Adaptive Large Margin Training for Multilabel Classification
"... Multilabel classification is a central problem in many areas of data analysis, including text and multimedia categorization, where individual data objects need to be assigned multiple labels. A key challenge in these tasks is to learn a classifier that can properly exploit label correlations without ..."
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Cited by 12 (9 self)
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Multilabel classification is a central problem in many areas of data analysis, including text and multimedia categorization, where individual data objects need to be assigned multiple labels. A key challenge in these tasks is to learn a classifier that can properly exploit label correlations without requiring exponential enumeration of label subsets during training or testing. We investigate novel loss functions for multilabel training within a large margin framework—identifying a simple alternative that yields improved generalization while still allowing efficient training. We furthermore show how covariances between the label models can be learned simultaneously with the classification model itself, in a jointly convex formulation, without compromising scalability. The resulting combination yields state of the art accuracy in multilabel webpage classification.
Graded Multilabel Classification: The Ordinal Case
"... We propose a generalization of multilabel classification that we refer to as graded multilabel classification. The key idea is that, instead of requesting a yesno answer to the question of class membership or, say, relevance of a class label for an instance, we allow for a graded membership of an i ..."
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Cited by 11 (2 self)
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We propose a generalization of multilabel classification that we refer to as graded multilabel classification. The key idea is that, instead of requesting a yesno answer to the question of class membership or, say, relevance of a class label for an instance, we allow for a graded membership of an instance, measured on an ordinal scale of membership degrees. This extension is motivated by practical applications in which a graded or partial class membership is natural. Apart from introducing the basic setting, we propose two general strategies for reducing graded multilabel problems to conventional (multilabel) classification problems. Moreover, we address the question of how to extend performance metrics commonly used in multilabel classification to the graded setting, and present first experimental results. 1.