Ensemble Selection based on Classifier Prediction Confidence

Nguyen, Tien Thanh; Luong, Anh Vu; Dang, Manh Truong; Liew, Alan Wee‐Chung; McCall, John

doi:10.1016/j.patcog.2019.107104

Cited by 53 publications

(21 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in which • returns 1 if the condition is true, otherwise returns 0. This loss function is for the classification error rate which is one of the most popular performance metrics in the literature [21]- [23]. The loss on the training set associated with w is given by:…”

Section: Proposed Methods a General Descriptionsmentioning

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

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.

show abstract

Section: Proposed Methods a General Descriptionsmentioning

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

show abstract

“…It stops adding models into the ensemble when the ensemble's performance starts to decrease after achieving the best performance. Ensemble selection allows ensembles to be optimized to performance metrics such as accuracy, cross-entropy, mean precision, or ROC Area (Ballard & Wang, 2016;Nguyen et al, 2020).…”

Section: Ensemble Selectionmentioning

confidence: 99%

“…In a recent study, Ensemble Selection outperforms the other ensemble models to classify 62 datasets (Nguyen et al, 2020).…”

Section: Ensemble Selectionmentioning

confidence: 99%

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Akhter

Zheng

Afzal

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors’ predictions to improve the fake news detection system’s overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.

show abstract

“…Ensemble can be divided into homogeneous ensemble and heterogeneous ensemble according to whether it is composed of the same algorithm [23]. Homogeneous ensemble includes bagging and boosting.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Zhang

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry to improve the reliability of the systems. Most data-based methods are limited to a single model, which is susceptible to various factors like environmental variability and diversity of operating conditions. In this paper, we propose an optimal stacking ensemble method combining different learning algorithms as meta-learners to mitigate the impact of multi-operating conditions. The selection of meta-learners follows a multi-objective evolutionary algorithm named non-dominated sorting genetic algorithms-II to balance the two conflicting objectives in terms of accuracy and diversity. Then the eventually evolved meta-learners are integrated by the meta-classifier for RUL estimation. In addition, a long-short-term feature extraction strategy is proposed to capture more degradation information from lifecycle data dynamically. Extensive experiments are performed on aero-engine dataset and battery dataset provided by NASA, which achieves the higher prognostic accuracy compared with the single models and existing methods. INDEX TERMS Remaining useful life (RUL), reliability, optimal stacking ensemble, meta-learners.

show abstract

Ensemble Selection based on Classifier Prediction Confidence

Cited by 53 publications

References 34 publications

WEC: Weighted Ensemble of Text Classifiers

WEC: Weighted Ensemble of Text Classifiers

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Contact Info

Product

Resources

About