Dynamic ensemble pruning based on multi-label classification

Markatopoulou, Fotini; Tsoumakas, Grigorios; Vlahavas, Ioannis

doi:10.1016/j.neucom.2014.07.063

Cited by 30 publications

(6 citation statements)

References 30 publications

(57 reference statements)

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“…In the past years, an additional intermediate step, consisting in selecting only some models before combining them, has been studied in depth [9]. The goal of this process, named as ensemble pruning [10,11,12] or ensemble selection [13,14,15], depending on the author, is to improve ensembles' accuracy and complexity by just using some models instead of all [16]. En-semble model selection can be further divided into two phases: 1) to define a function or criterion for evaluating/ranking the models and 2) to use a search algorithm to find the best group of models.…”

Section: Introductionmentioning

confidence: 99%

Dynamic ensemble selection for quantification tasks

et al. 2019

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Ensembles are among the most effective and successful methods for almost all supervised tasks. Not long ago, an ensemble approach has been proposed for quantification learning. The idea of such method is to exploit the prior knowledge about quantification tasks, building ensembles in which diversity is achieved by training each model with a different distribution. These training samples are generated taking into account the expected drift in class distribution. This paper extends this method proposing three new quantifier selection criteria particularly devised for quantification problems, where two of them are defined for dynamic ensemble selection. The experiments demonstrate that, in many cases, these selection functions outperform straightforward approaches, like averaging all models and using quantification accuracy to prune the ensemble. Moreover, the results show that performance heavily depends on the combination of the base quantification algorithm and the selection measure.

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Section: Introductionmentioning

confidence: 99%

Dynamic ensemble selection for quantification tasks

et al. 2019

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“…Zhou et al 32 considered the interactive functions between features and designed an online streaming feature selection based on feature interaction. Multi‐label learning studies the problem of each object containing multiple categories simultaneously 33–35 . For multi‐label data, Liu et al 36 combined online group selection and online inter‐group selection, and designed a criterion in group selection to select feature groups that are important to the label set.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-label learning studies the problem of each object containing multiple categories simultaneously. [33][34][35] For multi-label data, Liu et al 36 combined online group selection and online inter-group selection, and designed a criterion in group selection to select feature groups that are important to the label set. You et al 37 developed a new online multi-label streaming feature selection scheme that considers label correlation.…”

Section: Introductionmentioning

confidence: 99%

Online streaming feature selection based on hierarchical structure information

Lin,

Wang,

et al. 2024

Concurrency and Computation

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SummaryHierarchical classification learning aims to exploit the hierarchical relationship between data categories. The high dimensionality and dynamic of the data feature space are the main challenges of this research. Hierarchical feature selection uses a hierarchical structure to divide large‐scale tasks into multiple small tasks, which can more effectively improve the training speed and prediction accuracy of classification models. To present, existing online feature selection methods ignore the hierarchical structure of data. In addition, the dependency relationships in the hierarchical structure can serve as auxiliary knowledge to aid feature selection. Based on this, this paper proposes an online streaming hierarchical feature selection method based on kernelized fuzzy rough sets (OFS‐HNFRS). First, we use the prior knowledge of the hierarchical structure to divide the sample set into multiple subsets. Second, the dependency relationship of hierarchical structure is extended to kernelized fuzzy rough sets, and hierarchical category dependency based on kernelized fuzzy rough sets is defined. Finally, a new online feature selection framework is proposed, which is used to evaluate the relevance, significance, and redundancy of features. We verify the effectiveness of the proposed algorithm on six hierarchical datasets and eight flat datasets.

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“…A dynamic ensemble selection algorithm is proposed based on group method of data handling, and it has strong noise‐immunity ability 14 . Besides, some methods are presented from the perspectives of meta‐learning and multi‐label classification, respectively 17–19 . Recently, dynamic ensemble selection are used in quantification tasks, 20 imbalance learning, 21 and one‐versus‐one scheme of multi‐class classification problems 22 .…”

Section: Introductionmentioning

confidence: 99%

Prediction confidence‐based dynamic selection and weighted integration

et al. 2018

Concurrency and Computation

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Summary Ensemble learning is a successful learning paradigm and has gained considerable attention due to good generalization performance. Selection and integration of base learners are two key issues to construct an ensemble system. In this paper, a new kind framework of dynamic selection and weighted integration is proposed based on prediction confidence. In order to predict a sample, the base learners whose prediction confidences on the sample greater than or equal to a specified threshold are selected, and a final prediction is made based on the selected base learners with weighted integration. In weighted integration, the weight of a learner is computed based on its prediction confidence. Since the prediction confidences on different samples are different, the selected base learners as well as the weights are usually different for different samples. The computation of threshold is a key to the proposed framework. In classification ensemble, the threshold is computed via minimization of margin loss. In regression ensemble, the threshold is computed via minimization of prediction error. We analyze the rationality of the proposed framework in detail, and compare it with some other popular methods based on UCI classification tasks and short‐term wind speed prediction, respectively. Experimental results validate its effectiveness.

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Dynamic ensemble pruning based on multi-label classification

Cited by 30 publications

References 30 publications

Dynamic ensemble selection for quantification tasks

Dynamic ensemble selection for quantification tasks

Online streaming feature selection based on hierarchical structure information

Prediction confidence‐based dynamic selection and weighted integration

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