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162
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.
A Review on Multi-Label Learning Algorithms
"... Multi-label 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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Multi-label 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 state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label 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 multi-label learning are outlined for reference purposes.
Active learning by querying informative and representative examples
- in Advances in Neural Information Processing Systems (NIPS'10
, 2010
"... Most active learning approaches select either informative or representative unla-beled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usu-ally ad hoc in finding unlabeled instances that are bot ..."
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Cited by 34 (4 self)
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Most active learning approaches select either informative or representative unla-beled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usu-ally ad hoc in finding unlabeled instances that are both informative and repre-sentative. We address this challenge by a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and represen-tativeness of an instance. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches. 1
Reverse multi-label learning
- Advances in Neural Information Processing Systems 23
, 2010
"... Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction. In this paper we present a formulation for this problem based on reverse predi ..."
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Cited by 29 (2 self)
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Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction. In this paper we present a formulation for this problem based on reverse prediction: we predict sets of instances given the labels. By viewing the problem from this perspective, the most popular quality measures for assessing the performance of multi-label classification admit relaxations that can be efficiently optimised. We optimise these relaxations with standard algorithms and compare our results with several stateof-the-art methods, showing excellent performance. 1
Bayesian chain classifiers for multidimensional classification
- In Proceedings of the 22nd International Joint Conference on Artificial Intelligence
, 2011
"... In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defin-ing a compound class variable with all the possi-ble combinations of classes (label power-set meth-ods, LPMs) or by building independent classifiers for ..."
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Cited by 22 (2 self)
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In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defin-ing a compound class variable with all the possi-ble combinations of classes (label power-set meth-ods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ig-nore the dependency relations between classes. We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classi-fier chains and Bayesian networks for multidimen-sional classification. The method consists of two phases. In the first phase, a Bayesian network (BN) that represents the dependency relations between the class variables is learned from data. In the sec-ond phase, several chain classifiers are built, such that the order of the class variables in the chain is consistent with the class BN. At the end we combine the results of the different generated or-ders. Our method considers the dependencies be-tween class variables and takes advantage of the conditional independence relations to build simpli-fied models. We perform experiments with a chain of naı̈ve Bayes classifiers on different benchmark multidimensional datasets and show that our ap-proach outperforms other state-of-the-art methods. 1
Submodular Multi-Label Learning
"... In this paper we present an algorithm to learn a multi-label classifier which attempts at directly optimising the F-score. The key novelty of our formulation is that we explicitly allow for assortative (submodular) pairwise label interactions, i.e., we can leverage the co-ocurrence of pairs of label ..."
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Cited by 16 (1 self)
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In this paper we present an algorithm to learn a multi-label classifier which attempts at directly optimising the F-score. The key novelty of our formulation is that we explicitly allow for assortative (submodular) pairwise label interactions, i.e., we can leverage the co-ocurrence of pairs of labels in order to improve the quality of prediction. Prediction in this model consists of minimising a particular submodular set function, what can be accomplished exactly and efficiently via graph-cuts. Learning however is substantially more involved and requires the solution of an intractable combinatorial optimisation problem. We present an approximate algorithm for this problem and prove that it is sound in the sense that it never predicts incorrect labels. We also present a nontrivial test of a sufficient condition for our algorithm to have found an optimal solution. We present experiments on benchmark multi-label datasets, which attest the value of the proposed technique. We also make available source code that enables the reproduction of our experiments. 1
Multilabel learning by exploiting label correlations locally
- In AAAI
, 2012
"... It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assum-ing that the label correlations are shared by all the in-stances. In real-world tasks, however, different instances may share differe ..."
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Cited by 14 (2 self)
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It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assum-ing that the label correlations are shared by all the in-stances. In real-world tasks, however, different instances may share different label correlations, and few correla-tions are globally applicable. In this paper, we propose the ML-LOC approach which allows label correlations to be exploited locally. To encode the local influence of label correlations, we derive a LOC code to enhance the feature representation of each instance. The global dis-crimination fitting and local correlation sensitivity are incorporated into a unified framework, and an alternat-ing solution is developed for the optimization. Experi-mental results on a number of image, text and gene data sets validate the effectiveness of our approach.
On label dependence in multi-label 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 multi-label 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 multi-label 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 state-of-the-art algorithms for MLC and categorize them according to the type of label dependence they seek to capture. 1.
Multi-label hypothesis reuse
- In KDD
"... Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts. It is well-accepted that, in order to achieve a good performance, the relationship among labels should be exploited. Most existing approaches require the label relationship as prior k ..."
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Cited by 10 (4 self)
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Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts. It is well-accepted that, in order to achieve a good performance, the relationship among labels should be exploited. Most existing approaches require the label relationship as prior knowledge, or exploit by counting the label co-occurrence. In this paper, we propose the MAHR approach, which is able to automatically discover and exploit label relationship. Our basic idea is that, if two labels are related, the hypothesis generated for one label can be helpful for the other label. MAHR implements the idea as a boosting approach with a hypothesis reuse mechanism. In each boosting round, the base learner for a label is generated by not only learning on its own task but also reusing the hypotheses from other labels, and the amount of reuse across labels provides an es-timate of the label relationship. Extensive experimental re-sults validate that MAHR is able to achieve superior perfor-mance and discover reasonable label relationship. Moreover, we disclose that the label relationship is usually asymmetric.
