Evaluating the performance of bagging-based k-nearest neighbor ensemble with the voting rule selection method

Suchithra, M. S.; Pai, Maya L.

doi:10.1007/s11042-022-12716-3

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…Suchithra and Pai [112] used the nearest neighbour estimation and bagging ensemble method to propose an ensemble learner. Through implementing k-nearest label ranker, an ensemble model was proposed.…”

Section: Related Workmentioning

confidence: 99%

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Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.

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Section: Related Workmentioning

confidence: 99%

“…Araz and Spannowsky [111] proposed a combine-and-conquer that is based on Bayesian ensemble neural networks. Suchithra and Pai [112] evaluated the performance of bagging-based k-nearest. neighbor and proposed a voting rule method.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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“…bagging (Aledo et al, 2017;Suchithra & Pai, 2022), boosting (Dery & Shmueli, 2020) and random forest (de Sá et al, 2017;Zhou & Qiu, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Algorithms belonging to different machine learning paradigms (Zhou et al, 2014) have been proposed to tackle the LR problem: instance‐based learning (Cheng et al, 2009; Cheng et al, 2010), decision/regression trees (Cheng et al, 2009; de Sá et al, 2017; Plaia & Sciandra, 2019), neural networks (Ribeiro et al, 2012), association rules (de Sá et al, 2011), probabilistic graphical models (Rodrigo et al, 2021), and transformation methods (Brinker & Hüllermeier, 2020; Cheng et al, 2013; Hüllermeier et al, 2008). However, current state‐of‐the‐art methods are those based on the ensemble technique and, in particular, ensembles of Label Ranking Trees, which have been proposed for standard ensemble techniques: bagging (Aledo et al, 2017; Suchithra & Pai, 2022), boosting (Dery & Shmueli, 2020) and random forest (de Sá et al, 2017; Zhou & Qiu, 2018).…”

Section: Introductionmentioning

confidence: 99%

Efficient ensembles of distance‐based label ranking trees

Rodrigo,

Alfaro,

Aledo

et al. 2023

Expert Systems

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Ensemble of label ranking trees (LRTs) are currently the state‐of‐the‐art approaches to the label ranking problem. Recently, bagging, boosting, and random forest methods have been proposed, all based on the LRT algorithm, which adapts regression/classification trees to the label classification problem. The LRT algorithm uses theoretically grounded Mallows probability distribution to select the best split when growing the tree, and an EM‐type process to complete the rankings on the training data when they are incomplete. These two steps have proven to be accurate, but require a large computational effort. This article proposes two alternative methods that replace the use of the Mallows distribution with distance‐based criteria to select the best split at each inner node of the tree. Moreover, these distance‐based criteria allow dealing with incomplete rankings natively, so avoiding the completion process. We have carried out an extensive experimental evaluation, which shows that (1) the integration of the two proposed modifications to the LRT algorithm into ensemble methods (bagging and random forest) are an order of magnitude faster than using the original Mallows‐based LRT algorithm; (2) ensembles using the proposed LRT methods are significantly more accurate in the presence of incomplete rankings, while they are at least as accurate in the complete case; and (3) the two modified LRT algorithms are also an order of magnitude faster than the Mallows‐based LRT, while they are at least as accurate as the Mallows‐based LRT on both complete and incomplete rankings.

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Improving Alzheimer’s classification using a modified Borda count voting method on dynamic ensemble classifiers

Muhammed Niyas,

Paramasivan

2024

Knowl Inf Syst

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Evaluating the performance of bagging-based k-nearest neighbor ensemble with the voting rule selection method

Cited by 5 publications

References 52 publications

Ensemble learning based on relative accuracy approach and diversity teams

Ensemble learning based on relative accuracy approach and diversity teams

Efficient ensembles of distance‐based label ranking trees

Improving Alzheimer’s classification using a modified Borda count voting method on dynamic ensemble classifiers

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