Dynamic ensemble selection methods for heterogeneous data mining

Ballard, Chris; Wang, Wenjia

doi:10.1109/wcica.2016.7578244

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…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%

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

Akhter

Zheng

Afzal

et al. 2021

PeerJ Computer Science

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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.

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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

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“…Ballard and Wang [16] developed a dynamic ensemble selection methods for heterogeneous data mining. Although their datasets are not multimedia, their basic idea of combining multiple datasets at decision level inspired this work.…”

Section: Related Workmentioning

confidence: 99%

Generalised Decision Level Ensemble Method for Classifying Multi-media Data

Alyahyan

Wang

2018

Lecture Notes in Computer Science

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In recent decades, multimedia data have been commonly generated and used in various domains, such as in healthcare and social media due to their ability of capturing rich information. But as they are unstructured and separated, how to fuse and integrate multimedia datasets and then learn from them eectively have been a main challenge to machine learning. We present a novel generalised decision level ensemble method (GDLEM) that combines the multimedia datasets at decision level. After extracting features from each of multimedia datasets separately, the method trains models independently on each media dataset and then employs a generalised selection function to choose the appropriate models to construct a heterogeneous ensemble. The selection function is dened as a weighted combination of two criteria: the accuracy of individual models and the diversity among the models. The framework is tested on multimedia data and compared with other heterogeneous ensembles. The results show that the GDLEM is more exible and eective.

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“…Since local accuracy is a key feature of the DES method, many algorithms use k-nearest neighbors as a framework [19, 26, 27]. Other methods for generating different homogeneous classifiers have been proposed, including random subspace [28], bagging [29], boosting [30], and clustering [31].…”

Section: Introductionmentioning

confidence: 99%

A New Classification Method Based on Dynamic Ensemble Selection and its Application to Predict Variance Patterns in HIV-1 Env

Fili¹,

Han

Kort

et al. 2022

Preprint

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Therapeutics that target the envelope glycoproteins (Envs) of human immunodeficiency virus type 1 (HIV-1) effectively reduce virus levels in patients. However, due to mutations, new Env variants are frequently generated, which may be resistant to the treatments. The appearance of such sequence variance at any Env position is seemingly random. A better understanding of the spatiotemporal patterns of variance across Env may lead to the development of new therapeutic strategies. We hypothesized that, at any time point in a patient, positions with sequence variance are clustered on the three-dimensional structure of Env. To test this hypothesis, we examined whether variance at any Env position can be predicted by the variance measured at adjacent positions. Sequences from 300 HIV-infected patients were applied to a new algorithm we developed. The k-best classifiers (KBC) method is a dynamic ensemble selection technique that identifies the best classifier(s) within the neighborhood of a new observation. It applies bootstrap resampling to generate out-of-bag samples that are used with the resampled set to evaluate each classifier. For many positions of Env, primarily in the CD4-binding site, KBC accurately predicted variance based on the variance at their adjacent positions. KBC improved performance compared to the initial learners, static ensemble, and other baseline models. KBC also outperformed other algorithms for predicting variance at multi-position footprints of therapeutics on Env. These understandings can be applied to refine models that predict future changes in HIV-1 Env. More generally, we propose KBC as a new high-performance dynamic ensemble selection technique.

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Dynamic ensemble selection methods for heterogeneous data mining

Cited by 16 publications

References 14 publications

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

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

Generalised Decision Level Ensemble Method for Classifying Multi-media Data

A New Classification Method Based on Dynamic Ensemble Selection and its Application to Predict Variance Patterns in HIV-1 Env

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