Adaptive Selection of Base Classifiers in One-Against-All Learning for Large Multi-labeled Collections

Ráez, Arturo Montejo; López, Luís Alfonso Ureña; Steinberger, Ralf

doi:10.1007/978-3-540-30228-5_1

Cited by 14 publications

(5 citation statements)

References 6 publications

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“…In Ráez et al (2004), the issue of class-label imbalance is identified with respect to large text categorisation problems tackled with BR, caused by the inevitable label sparsity, which yields a relatively small number of positive examples in the transformed problems. This work addresses this issue by overweighting positive examples in each of the BR models.…”

Section: Related Methodsmentioning

confidence: 99%

Classifier chains for multi-label classification

et al. 2011

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The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

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Section: Related Methodsmentioning

confidence: 99%

Classifier chains for multi-label classification

et al. 2011

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“…Countermeasures to this are possible, for example by using per-label thresholding methods or classifier weightings as in Ráez et al (2004).…”

Section: Binary Relevance (Br)-based Methods In Data Stream Settingsmentioning

confidence: 99%

Scalable and efficient multi-label classification for evolving data streams

et al. 2012

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Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while being ready to predict at any point. This paper proposes a new experimental framework for learning and evaluating on multilabel data streams, and uses it to study the performance of various methods. From this study, we develop a multi-label Hoeffding tree with multi-label classifiers at the leaves. We show empirically that this method is well suited to this challenging task. Using our new framework, which allows us to generate realistic multi-label data streams with concept drift (as well as real data), we compare with a selection of baseline methods, as well as new learning methods from the literature, and show that our Hoeffding tree method achieves fast and more accurate performance.

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“…The columns Inst, Attr and Lbl are respectively the number of instances, attributes and labels. Label sets (lSets) is the amount of distinct label combination, proportion of unique label sets (PUL) indicates the proportion of label sets related to a single instance, label cardinality (lCard) measures the average number of labels per instance, label density (lDen) describes the average frequency of labels, dependency (Dep) shows the average unconditional labels' dependency (Luaces et al 2012), inner imbalance degree (IID) measures the average label imbalance in the binary data sets (Raez et al 2004) and, finally, correlation (Corr) indicates the average correlation between the predictive attributes and the labels.…”

Section: Data Setsmentioning

confidence: 99%

An empirical analysis of binary transformation strategies and base algorithms for multi-label learning

et al. 2020

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Investigating strategies that are able to efficiently deal with multi-label classification tasks is a current research topic in machine learning. Many methods have been proposed, making the selection of the most suitable strategy a challenging issue. From this premise, this paper presents an extensive empirical analysis of the binary transformation strategies and base algorithms for multi-label learning. This subset of strategies uses the one-versus-all approach to transform the original data, generating one binary data set per label, upon which any binary base algorithm can be applied. Considering that the influence of the base algorithm on the predictive performance obtained by the strategies has not been considered in depth by many empirical studies, we investigated the influence of distinct base algorithms on the performance of several strategies. Thus, this study covers a family of multi-label strategies using a diversified range of base algorithms, exploring their relationship over different perspectives. This finding has significant implications concerning the methodology of evaluation adopted in multi-label experiments containing binary transformation strategies, given that multiple base algorithms should be considered. Despite these improvements in strategy and base algorithms, for many data sets, a large number of labels, mainly those less frequent, were either never predicted, or always misclassified. We conclude the experimental analysis by recommending strategies and base algorithms in accordance with different performance criteria.

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Adaptive Selection of Base Classifiers in One-Against-All Learning for Large Multi-labeled Collections

Cited by 14 publications

References 6 publications

Classifier chains for multi-label classification

Classifier chains for multi-label classification

Scalable and efficient multi-label classification for evolving data streams

An empirical analysis of binary transformation strategies and base algorithms for multi-label learning

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