A weighted multiple classifier framework based on random projection

Nguyen, Tien Thanh; Dang, Manh Truong; Liew, Alan Wee‐Chung; Bezdek, James C.

doi:10.1016/j.ins.2019.03.067

Cited by 28 publications

(21 citation statements)

References 37 publications

(37 reference statements)

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“…This type of ensemble focuses on designing the combining algorithm that combines the outputs of the learners. One well-known heterogeneous ensemble approach is Stacking which trains the combining algorithm on the predictive outputs of training observations [16].…”

Section: B Ensemble Methods For Text Classificationmentioning

confidence: 99%

WEC: Weighted Ensemble of Text Classifiers

Upadhyay

Nguyen

Massie

et al. 2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the classification model. Traditional machine learning methods represent documents in vector space using features such as term frequencies, which have limitations in handling the order and semantics of words. Meanwhile, although achieving many successes, deep learning classifiers require substantial resources in terms of labelled data and computational complexity. In this work, a weighted ensemble of classifiers (WEC) is introduced to address the text classification problem. Instead of using majority vote as the combining method, we propose to associate each classifier's prediction with a different weight when combining classifiers. The optimal weights are obtained by minimising a loss function on the training data with the Particle Swarm Optimisation algorithm. We conducted experiments on 5 popular datasets and report classification performance of algorithms with classification accuracy and macro F1 score. WEC was run with several different combinations of traditional machine learning and deep learning classifiers to show its flexibility and robustness. Experimental results confirm the advantage of WEC, especially on smaller datasets.

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Section: B Ensemble Methods For Text Classificationmentioning

confidence: 99%

WEC: Weighted Ensemble of Text Classifiers

Upadhyay

Nguyen

Massie

et al. 2020

2020 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

View full text Add to dashboard Cite

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“…run 10-fold cross validation 3 times to obtain 30 test results for each dataset (presented in Table 2). The non-parametric two-tailed Wilcoxon signed-rank test [10] was used to compare the experimental results of the proposed method and a benchmark algorithm on a particular dataset in which p-value < 0.05 deems as difference in experimental results is significant.…”

Section: Methodsmentioning

confidence: 99%

“…By measuring the similarity between two learning tasks, the performance ranking of the trained classifiers of a given learning task can be inferred so as to obtain the optimal combining weights of the trained classifiers. Nguyen et al [10] weighed the base classifiers generated on projected data of training observations by the linear regression model.…”

Section: Background and Related Workmentioning

confidence: 99%

Confidence in Prediction: An Approach for Dynamic Weighted Ensemble

Thuan

Nguyen

Luong

et al. 2020

Intelligent Information and Database Systems

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Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier's output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods.

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“…Another recent solution for weighting classifiers is found in Reference . They proposed a weighted multiple classifier framework based on random projections (WMCRP).…”

Section: Related Workmentioning

confidence: 99%

CSBF: A static ensemble fusion method based on the centrality score of complex networks

Silva

Britto

Enembreck

et al. 2019

Computational Intelligence

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Ensemble of classifiers can improve classification accuracy by combining several models. The fusion method plays an important role in the ensemble performance. Usually, a criterion for weighting the decision of each ensemble member is adopted. Frequently, this can be done using some heuristic based on accuracy or confidence. Then, the used fusion rule must consider the established criterion for providing a most reliable ensemble output through a kind of competition among the ensemble members. This article presents a new ensemble fusion method, named centrality score‐based fusion, which uses the centrality concept in the context of social network analysis (SNA) as a criterion for the ensemble decision. Centrality measures have been applied in the SNA to measure the importance of each person inside of a social network, taking into account the relationship of each person with all others. Thus, the idea is to derive the classifier weight considering the overall classifier prominence inside the ensemble network, which reflects the relationships among pairs of classifiers. We hypothesized that the prominent position of a classifier based on its pairwise relationship with the other ensemble members could be its weight in the fusion process. A robust experimental protocol has confirmed that centrality measures represent a promising strategy to weight the classifiers of an ensemble, showing that the proposed fusion method performed well against the literature.

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A weighted multiple classifier framework based on random projection

Cited by 28 publications

References 37 publications

WEC: Weighted Ensemble of Text Classifiers

WEC: Weighted Ensemble of Text Classifiers

Confidence in Prediction: An Approach for Dynamic Weighted Ensemble

CSBF: A static ensemble fusion method based on the centrality score of complex networks

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