A New Ensemble Learning Algorithm Based on Improved K-Means for Training Neural Network Ensembles

Zhi-Gang, Gan

doi:10.1109/iitsi.2009.8

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The main focus with the bagging technique is related to increasing diversity among base classifiers. In recent studies, there were some successful solutions proposed based on applying some selection criteria rather than applying all base classifiers (Datta & Pihur, 2010; Zeng et al ., 2010) or performing a clustering process instead of random sampling to obtain different training sets (Gan & Xiao, 2009). Most of the recent study regarding boosting techniques focused on adapting it to stable classifiers, with the main focus on k -NN classifier.…”

Section: Discussionmentioning

confidence: 99%

“…According to the experimental results, all the measures significantly improved the performance of MV. Different techniques such as applying disparate feature subsets (Bryll et al ., 2003), different training sets obtained by clustering (Gan & Xiao, 2009) and random selection (Breiman, 1996), have been suggested to increase diversity.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…A method that involved the use of clustering during the pre-processing phase was introduced by Gan and Xiao (2009). In that work, each model was trained with a subset of the original training data set.…”

Section: Ensemble Generationmentioning

confidence: 99%

See 2 more Smart Citations

A survey of commonly used ensemble-based classification techniques

Jurek¹,

Bi²,

Wu³

et al. 2013

The Knowledge Engineering Review

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The combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts, which have been made to improve their performance. Within this paper, we present and compare an updated view on the different modifications of these techniques, which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting. In addition, we provide a review of different ensemble selection methods based on both static and dynamic approaches. We present some new directions which have been adopted in the area of classifier ensembles from a range of recently published studies. In order to provide a deeper insight into the ensembles themselves a range of existing theoretical studies have been reviewed in the paper.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

A survey of commonly used ensemble-based classification techniques

Jurek¹,

Bi²,

Wu³

et al. 2013

The Knowledge Engineering Review

View full text Add to dashboard Cite

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Building Ensembles with Classifier Selection Using Self-Organizing Maps

Almeida

Zanchettin

Leite³

2016

2016 5th Brazilian Conference on Intelligent Systems (BRACIS)

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Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

Gai

et al. 2021

ACM Trans. Internet Technol.

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In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

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A New Ensemble Learning Algorithm Based on Improved K-Means for Training Neural Network Ensembles

Cited by 3 publications

References 5 publications

A survey of commonly used ensemble-based classification techniques

A survey of commonly used ensemble-based classification techniques

Building Ensembles with Classifier Selection Using Self-Organizing Maps

Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

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