Review of ensembles of multi-label classifiers: Models, experimental study and prospects

Moyano, Jose M.; Gibaja, Eva; Cios, Krzysztof J.; Ventura, Sebastián

doi:10.1016/j.inffus.2017.12.001

Cited by 133 publications

(93 citation statements)

References 34 publications

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“…In the literature, besides the practical applications of ensemble methods in many areas, research on ensemble methods can be divided into three aspects: information for the trained classifiers of a given learning task can be inferred so as to obtain the optimal combining weights of the trained classifiers. Moreover, several ensemble systems were developed for different learning paradigms such as incremental learning [30][31][32], semisupervised learning [33], and multi-label learning [34,35]. For instance, Pham et al [31] combined random projections and Hoeffding tree to construct an incremental online ensemble learning system.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Combining heterogeneous classifiers via granular prototypes

Nguyen

Pham

et al. 2018

Applied Soft Computing

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In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e.

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Section: Ensemble Methodsmentioning

confidence: 99%

Combining heterogeneous classifiers via granular prototypes

Nguyen

Pham

et al. 2018

Applied Soft Computing

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“…-ML. The most relevant algorithm adaptations [118] are based on standard classification algorithms with added support for choosing more than one class at a time: adaptations exist for k-NN [117], decision trees [24], SVMs [36], association rules [106] and ensembles [82].…”

Section: Algorithm Adaptationmentioning

confidence: 99%

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

Charte

García

et al. 2018

Prog Artif Intell

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Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g. classification and regression) and unsupervised learning (e.g. clustering and association rules).Within supervised learning, most studies and research are focused on well known standard tasks, such as binary classification, multiclass classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find.The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them and their main applications is provided as well.

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“…Techniques for reducing MLC to SLC problems involve the choice of a base learner for solving the latter. Somewhat surprisingly, this choice is often neglected, despite having an important influence on generalization performance [10][11][12]15]. Even in more extensive studies [10,12], a base learner is fixed a priori in a more or less arbitrary way.…”

Section: Introductionmentioning

confidence: 99%

“…Somewhat surprisingly, this choice is often neglected, despite having an important influence on generalization performance [10][11][12]15]. Even in more extensive studies [10,12], a base learner is fixed a priori in a more or less arbitrary way. Broader studies considering multiple base learners, such as [6,22], are relatively rare and rather limited in terms of the number of base learners considered.…”

Section: Introductionmentioning

confidence: 99%

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification

Wever

Tornede

Mohr

et al. 2020

Lecture Notes in Computer Science

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In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification, where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straightforward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.

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Review of ensembles of multi-label classifiers: Models, experimental study and prospects

Cited by 133 publications

References 34 publications

Combining heterogeneous classifiers via granular prototypes

Combining heterogeneous classifiers via granular prototypes

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification

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