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109
MultiLabel Prediction via Compressed Sensing
, 902
"... We consider multilabel prediction problems with large output spaces under the assumption of output sparsity – that the target vectors have small support. We develop a general theory for a variant of the popular ECOC (error correcting output code) scheme, based on ideas from compressed sensing for e ..."
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Cited by 100 (3 self)
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We consider multilabel prediction problems with large output spaces under the assumption of output sparsity – that the target vectors have small support. We develop a general theory for a variant of the popular ECOC (error correcting output code) scheme, based on ideas from compressed sensing for exploiting this sparsity. The method can be regarded as a simple reduction from multilabel regression problems to binary regression problems. It is shown that the number of subproblems need only be logarithmic in the total number of label values, making this approach radically more efficient than others. We also state and prove performance guarantees for this method, and test it empirically. 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 92 (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.
KernelBased Learning of Hierarchical Multilabel Classification Models
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We present a kernelbased algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Mark ..."
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Cited by 78 (8 self)
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We present a kernelbased algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in singleexample subspaces spanned by the marginal dual variables. The optimization is facilitated with a dynamic programming based algorithm that computes best update directions in the feasible set. Experiments show
Decision trees for hierarchical multilabel classification
 Machine Learning
, 2008
"... Abstract. Hierarchical multilabel classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an em ..."
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Cited by 59 (2 self)
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Abstract. Hierarchical multilabel classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an empirical study of their use in functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to two approaches that learn a set of regular classification trees (one for each class). The first approach defines an independent singlelabel classification task for each class (SC). Obviously, the hierarchy introduces dependencies between the classes. While they are ignored by the first approach, they are exploited by the second approach, named hierarchical singlelabel classification (HSC). Depending on the application at hand, the hierarchy of classes can be such that each class has at most one parent (tree structure) or such that classes may have multiple parents (DAG structure). The latter case has not been considered before and we show how the HMC and HSC approaches can be modified to support this setting. We compare the three approaches on 24 yeast data sets using as classification schemes MIPS’s FunCat (tree structure) and the Gene Ontology (DAG structure). We show that HMC trees outperform HSC and SC trees along three dimensions: predictive accuracy, model size, and induction time. We conclude that HMC trees should definitely be considered in HMC tasks where interpretable models are desired. 1
Hierarchical classification: combining bayes with SVM
 IN: ICML ’06: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 2006
"... We study hierarchical classification in the general case when an instance could belong to more than one class node in the underlying taxonomy. Experiments done in previous work showed that a simple hierarchy of Support Vectors Machines (SVM) with a topdown evaluation scheme has a surprisingly good ..."
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Cited by 46 (4 self)
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We study hierarchical classification in the general case when an instance could belong to more than one class node in the underlying taxonomy. Experiments done in previous work showed that a simple hierarchy of Support Vectors Machines (SVM) with a topdown evaluation scheme has a surprisingly good performance on this kind of task. In this paper, we introduce a refined evaluation scheme which turns the hierarchical SVM classifier into an approximator of the Bayes optimal classifier with respect to a simple stochastic model for the labels. Experiments on synthetic datasets, generated according to this stochastic model, show that our refined algorithm outperforms the simple hierarchical SVM. On realworld data, however, the advantage brought by our approach is a bit less clear. We conjecture this is due to a higher noise rate for the training labels in the low levels of the taxonomy.
Refined Experts Improving Classification in Large Taxonomies
, 2009
"... While largescale taxonomies – especially for web pages – have been in existence for some time, approaches to automatically classify documents into these taxonomies have met with limited success compared to the more general progress made in text classification. We argue that this stems from three ca ..."
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Cited by 42 (0 self)
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While largescale taxonomies – especially for web pages – have been in existence for some time, approaches to automatically classify documents into these taxonomies have met with limited success compared to the more general progress made in text classification. We argue that this stems from three causes: increasing sparsity of training data at deeper nodes in the taxonomy, error propagation where a mistake made high in the hierarchy cannot be recovered, and increasingly complex decision surfaces in higher nodes in the hierarchy. While prior research has focused on the first problem, we introduce methods that target the latter two problems – first by biasing the training distribution to reduce error propagation and second by propagating up “firstguess” expert information in a bottomup manner before making a refined top down choice. Finally, we present an empirical study demonstrating that the suggested changes lead to 1030 % improvements in F1 scores versus an accepted competitive baseline, hierarchical SVMs.
Large Scale MaxMargin MultiLabel Classification with Priors
"... We propose a maxmargin formulation for the multilabel classification problem where the goal is to tag a data point with a set of prespecified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this ..."
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Cited by 41 (2 self)
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We propose a maxmargin formulation for the multilabel classification problem where the goal is to tag a data point with a set of prespecified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Existing solutions take either of two approaches. The first assumes, a priori, that there are no label correlations and independently trains a classifier for each label (as is done in the 1vsAll heuristic). This reduces the problem complexity from exponential to linear and such methods can scale to large problems. The second approach explicitly models correlations by pairwise label interactions. However, the complexity remains exponential unless one assumes that label correlations are sparse. Furthermore, the learnt correlations reflect the training set biases. We take a middle approach that assumes labels are correlated but does not incorporate pairwise label terms in the prediction function. We show that the complexity can still be reduced from exponential to linear while modelling dense pairwise label correlations. By incorporating correlation priors we can overcome training set biases and improve prediction accuracy. We provide a principled interpretation of the 1vsAll method and show
Learning Hierarchical MultiCategory Text Classification Models
 22nd International Conference on Machine Learning (ICML'2005
, 2005
"... jstatecs.soton.ac.uk ..."
Hedging Your Bets: Optimizing AccuracySpecificity Tradeoffs in Large Scale Visual Recognition
"... As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracyspecificity tradeoffs in large scale recognition, motivated by the observation that object categories form a semantic ..."
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Cited by 27 (7 self)
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As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracyspecificity tradeoffs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading off specificity for accuracy in case of uncertainty. By optimizing this tradeoff, we obtain classifiers that try to be as specific as possible while guaranteeing an arbitrarily high accuracy. We formulate the problem as maximizing information gain while ensuring a fixed, arbitrarily small error rate with a semantic hierarchy. We propose the Dual Accuracy Reward Tradeoff Search (DARTS) algorithm and prove that, under practical conditions, it converges to an optimal solution. Experiments demonstrate the effectiveness of our algorithm on datasets ranging from 65 to over 10,000 categories. 1.