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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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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.
Efficient multilabel classification algorithms for largescale problems in the legal domain
 IN: PROCEEDINGS OF THE LANGUAGE RESOURCES AND EVALUATION CONFERENCE (LREC) WORKSHOP ON SEMANTIC PROCESSING OF LEGAL TEXTS
, 2008
"... In this paper we evaluate the performance of multilabel classification algorithms on the EURLex database of legal documents of the European Union. On the same set of underlying documents, we defined three different largescale multilabel problems with up to 4000 classes. On these datasets, we compa ..."
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In this paper we evaluate the performance of multilabel classification algorithms on the EURLex database of legal documents of the European Union. On the same set of underlying documents, we defined three different largescale multilabel problems with up to 4000 classes. On these datasets, we compared three algorithms: (i) the wellknown oneagainstall approach (OAA); (ii) the multiclass multilabel perceptron algorithm (MMP), which modifies the OAA ensemble by respecting dependencies between the base classifiers in the training protocol of the classifier ensemble; and (iii) the multilabel pairwise perceptron algorithm (MLPP), which unlike the previous algorithms trains one base classifier for each pair of classes. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier. This makes them very suitable for largescale multilabel classification problems. While previous work has already shown that the latter approach outperforms the other two approaches in terms of predictive accuracy, its key problem is that it has to store one classifier for each pair of classes. The key contribution of this work is to demonstrate a novel technique that makes the pairwise approach feasible for problems with large number of classes, such as those studied in this work. Our results on the EURLex database illustrate the effectiveness of the pairwise approach and the efficiency of the MMP algorithm. We also show that it is feasible to efficiently and effectively handle very large multilabel problems.
Largescale multilabel text classificationrevisiting neural networks. arXiv preprint arXiv:1312.5419
, 2013
"... Abstract. Neural networks have recently been proposed for multilabel classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BPMLL, a neural network (NN) architecture that aims at minimizing pairwise ranking er ..."
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Abstract. Neural networks have recently been proposed for multilabel classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BPMLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for largescale multilabel text classification tasks. In particular, we show that BPMLL’s ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting. Our experimental results show that simple NN models equipped with advanced techniques such as rectified linear units, dropout, and AdaGrad perform as well as or even outperform stateoftheart approaches on six largescale textual datasets with diverse characteristics. 1
Two stage architecture for multilabel learning.
 Pattern Recognition
, 2012
"... a b s t r a c t A common approach to solving multilabel learning problems is to use problem transformation methods and dichotomizing classifiers as in the pairwise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for ..."
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a b s t r a c t A common approach to solving multilabel learning problems is to use problem transformation methods and dichotomizing classifiers as in the pairwise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with a large number of labels. To tackle this problem, we propose a Two Stage Architecture (TSA) for efficient multilabel learning. We analyze three implementations of this architecture the Two Stage Voting Method (TSVM), the Two Stage Classifier Chain Method (TSCCM) and the Two Stage Pruned Classifier Chain Method (TSPCCM). Eight different realworld datasets are used to evaluate the performance of the proposed methods. The performance of our approaches is compared with the performance of two algorithm adaptation methods (MultiLabel kNN and MultiLabel C4.5) and five problem transformation methods (Binary Relevance, Classifier Chain, Calibrated Label Ranking with majority voting, the Quick Weighted method for pairwise multilabel learning and the Label Powerset method). The results suggest that TSCCM and TSPCCM outperform the competing algorithms in terms of predictive accuracy, while TSVM has comparable predictive performance. In terms of testing speed, all three methods show better performance as compared to the pairwise methods for multilabel learning.
Hybrid Decision Tree Architecture utilizing Local SVMs for MultiLabel Classification
"... Abstract. Multilabel classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the largescale problem ..."
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Abstract. Multilabel classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the largescale problem and the high dimensionality of the label space, which have a strong impact on the computational complexity of learning. These problems are especially pronounced for approaches that transform MLC problems into a set of binary classification problems for which SVMs are used. On the other hand, the most efficient approaches to MLC, based on decision trees, have clearly lower predictive performance. We propose a hybrid decision tree architecture that utilizes local SVMs for efficient multilabel classification. We build decision trees for MLC, where the leaves do not give multilabel predictions directly, but rather contain SVMbased classifiers giving multilabel predictions. A binary relevance architecture is employed in each leaf, where a binary SVM classifier is built for each of the labels relevant to that particular leaf. We use several realworld datasets to evaluate the proposed method and its competition. Our hybrid approach on almost every classification problem outperforms the predictive performances of SVMbased approaches while its computational efficiency is significantly improved as a result of the integrated decision tree. Key words: multilabel classification, hybrid architecture 1
Multilabel Classification in Parallel Tasks
"... In real world multilabel problems, it is often the case that e.g. documents are simultaneously classified with labels from multiple domains, such as genres in addition to topics. In practice, each of these problems is solved independently without taking advantage of possible label correlations betwe ..."
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In real world multilabel problems, it is often the case that e.g. documents are simultaneously classified with labels from multiple domains, such as genres in addition to topics. In practice, each of these problems is solved independently without taking advantage of possible label correlations between domains. Following the multitask learning setting, in which multiple similar tasks are learned in parallel, we propose a global learning approach that jointly considers all domains. It is empirically demonstrated in this work that this approach is effective despite its simplicity when using a multilabel learner that takes label correlations into account. 1.
Efficient Prediction Algorithms for Binary Decomposition Techniques
"... Binary decomposition methods transform multiclass learning problems into a series of twoclass learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows superlinearly with the number of classes, we need efficient methods for c ..."
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Binary decomposition methods transform multiclass learning problems into a series of twoclass learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows superlinearly with the number of classes, we need efficient methods for computing the predictions. In this paper, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary errorcorrecting output codes under a variety of different code designs and decoding strategies.
Preference Learning and Ranking by Pairwise Comparison
"... This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learn ..."
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This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.
TWO STAGE CLASSIFIER CHAIN ARCHITECTURE FOR EFFICIENT PAIRWISE MULTILABEL LEARNING
"... A common approach for solving multilabel learning problems using problemtransformation methods and dichotomizing classifiers is the pairwise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction tha ..."
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A common approach for solving multilabel learning problems using problemtransformation methods and dichotomizing classifiers is the pairwise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with large number of labels. To tackle this problem we propose a Two Stage Classifier Chain Architecture (TSCCA) for efficient pairwise multilabel learning. Six different realworld datasets were used to evaluate the performance of the TSCCA. The performance of the architecture was compared with six methods for multilabel learning and the results suggest that the TSCCA outperforms the concurrent algorithms in terms of predictive accuracy. In terms of testing speed TSCCA shows better performance comparing to the pairwise methods for multilabel learning. Index Terms — Multilabel, two stage, learning, classification 1.