Dynamic Classifier Chains for Multi-label Learning

Trajdos, Pawel; Kurzyński, Marek

doi:10.1007/978-3-030-33676-9_40

Cited by 5 publications

(4 citation statements)

References 61 publications

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“…Comparison Methods. To report reliable comparison results, MLDE is compared with 4 state-of-the-art MLC ensemble methods proposed in recent years, including DECC (Trajdos and Kurzynski 2019), ACkEL (Wang et al 2021), MLWSE (Xia, Chen, and Yang 2021), BOOMER (Rapp et al 2020(Rapp et al , 2021 Hyper-Parameter Setting. For AdaBoost.C2, we set T = 10 as its upper limit of iteration rounds and δ = 0.01 as the lower limit of iteration error.…”

Section: Experiments Experimental Settingmentioning

confidence: 99%

“…Many methods propose to address this problem by heuristically providing a good chain order (Kajdanowicz and Kazienko 2013;Jun et al 2019). As an optimal order is hard to find, some ensemble methods (Read et al 2011;Trajdos and Kurzynski 2019), instead, provide an ensemble of CC learners in different orders to avoid the uncertainty of a single order. Unfortunately, so far, the error propagation problem, together with the chain order issue, have not been well solved (Read et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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AdaBoost.C2: Boosting Classifiers Chains for Multi-Label Classification

Zhu

Wang

2023

AAAI

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During the last decades, multi-label classification (MLC) has attracted the attention of more and more researchers due to its wide real-world applications. Many boosting methods for MLC have been proposed and achieved great successes. However, these methods only extend existing boosting frameworks to MLC and take loss functions in multi-label version to guide the iteration. These loss functions generally give a comprehensive evaluation on the label set entirety, and thus the characteristics of different labels are ignored. In this paper, we propose a multi-path AdaBoost framework specific to MLC, where each boosting path is established for distinct label and the combination of them is able to provide a maximum optimization to Hamming Loss. In each iteration, classifiers chain is taken as the base classifier to strengthen the connection between multiple AdaBoost paths and exploit the label correlation. Extensive experiments demonstrate the effectiveness of the proposed method.

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Section: Experiments Experimental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AdaBoost.C2: Boosting Classifiers Chains for Multi-Label Classification

Zhu

Wang

2023

AAAI

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“…However, the authors do not examine the difference between these two strategies. Trajdos and Kurzynski (2019) introduce dynamic chaining based on accuracy estimates of binary relevance predictions in a local neighborhood of the test example, and employ this technique for nearest neighbor and Naïve Bayes classifiers, but, in the latter case, have to make a conditional independence assumption on the label predictions as well.…”

Section: Related Workmentioning

confidence: 99%

Tree-Based Dynamic Classifier Chains

Mencía¹,

Kulessa²,

Bohlender³

et al. 2021

Preprint

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Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a naïve implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.

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“…Then, the final data representation of the first data representation   1 X , i.e., is obtained. (6) in which the output β is calculated by…”

Section: Hmentioning

confidence: 99%

ML-CK-ELM: An efficient Multi-layer Extreme Learning Machine using Combined Kernels for Multi-label classification

2020

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Recently many neural network methods have been proposed for multi-label classification in the literature. One of these recent researches is the multi-layer extreme learning machines (ML-ELM) in which stack auto encoders have been used for tuning the weights. However, ML-ELM suffers from three primary drawbacks: First, input weights and biases are chosen randomly. Second, the pseudoinverse solution for calculating output weights will cause to increase the reconstruction error. Third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper multi-layer kernel extreme learning machine (ML-CK-ELM), that uses a linear combination of base kernels in each layer, is proposed for multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, multi-label classification data inherently have multi-modal aspects due to the variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of ML-CK-ELM that leads to implicitly assess the inherent multi-modal aspects of multi-label data; each kernel can be effectively used to cover one of the modals better than the other kernels. The empirical study indicates that ML-CK-ELM represents competitive performance against other state-of-theart methods, and experimental results over multi-label datasets verify the feasibility of ML-CK-ELM.

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Dynamic Classifier Chains for Multi-label Learning

Cited by 5 publications

References 61 publications

AdaBoost.C2: Boosting Classifiers Chains for Multi-Label Classification

AdaBoost.C2: Boosting Classifiers Chains for Multi-Label Classification

Tree-Based Dynamic Classifier Chains

ML-CK-ELM: An efficient Multi-layer Extreme Learning Machine using Combined Kernels for Multi-label classification

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