Pruning the Ensemble of ANN Based on Decision Tree Induction

Ding, Sha; Chen, Zhi; Zhao, Shirong; Lin, Tao

doi:10.1007/s11063-017-9703-6

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…To verify the utility of the ECDPB, we compare the proposed method with the following techniques: GASEN [17], DMEP [20], MDOEP [39], IBAFSEN [19], PEAD [40], RCOA [34], and IDAFMEP [16]. The primary goal of these techniques is to enhance the classification capacity in comparison with a bagging ensemble.…”

Section: Resultsmentioning

confidence: 99%

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Zhu¹,

Xia²,

He³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account; it is used to identify the ELMs that have good diversity and low error. In addition, we propose an ECDPB with powerful optimizing ability; it is employed to find the optimal subset of ELMs. The selected ELMs can then be used to form an ensemble classifier. Experiments on 10 benchmark datasets have been conducted, and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

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Section: Resultsmentioning

confidence: 99%

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Zhu¹,

Xia²,

He³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…To test the classification ability of HEPCBR further, massive experiments were implemented by comparing it with other techniques: bagging [51], kappa [28], AGOB [26], POBE [27], DREP [22], DEELM [39], GASEN [33], GSOEP [34], MOAG [30], RREP [10], DMEP [40], EPSCG [20], DASEP [24], MDOEP [31], RCOA [32], and PEAD [41]. Bagging extracts the training samples with equal probability, and it can construct an initial pool composed of multiple learners with a good diversity.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Widely used techniques include k-means [35] and deterministic annealing [36]. Finally, many scholars study other different pruning methods to acquire the selection of learners, for example, frequent pattern [7], a randomized greedy selective strategy and ballot [37], greedy randomized dynamic pruning [38], confidence interval based on double-fault measure [39], graph coloring way [40], cost-sensitive rotation forest [17], induction of decision tree [41], and simple coalitional games [20].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Ensemble Pruning Using Coevolution Binary Glowworm Swarm Optimization and Reduce-Error

Zhu

Xia

et al. 2020

Complexity

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Ensemble pruning has been widely applied to improve the capacity of multiple learner system. Both diversity and classification accuracy of learners are considered as two key factors for achieving an ensemble with competitive classification ability. Considering that extreme learning machine (ELM) is characterized by excellent training rate and generalization capability, it is employed as the base classifier. For a multiple ELM system, when we increase its constituents’ diversity, the mean accuracy of the whole members must be decreased. Therefore, a compromise between them can ensure that the ELMs remain good diversity and high precision, but finding the compromise brings a heavy computational burden. It is hard to look for the exact result via the searching of intelligent algorithms or pruning of diversity measures. On the basis, we propose a hybrid ensemble pruning approach employing coevolution binary glowworm swarm optimization and reduce-error (HEPCBR). Considering the good performance of reduce-error (RE) in selecting ELMs with high diversity and precision, we try to employ RE to choose the satisfactory ELMs from the generated ELMs. In addition, the constituents are further selected via the proposed coevolution binary glowworm swarm optimization, which are utilized to construct the promising ensemble. Experimental results indicate that, compared to other frequently used methods, the proposed HEPCBR achieves significantly superior performance in classification.

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Semi-supervised Fuzzy Min–Max Neural Network for Data Classification

Liu

et al. 2019

Neural Process Lett

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Pruning the Ensemble of ANN Based on Decision Tree Induction

Cited by 8 publications

References 36 publications

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Hybrid Ensemble Pruning Using Coevolution Binary Glowworm Swarm Optimization and Reduce-Error

Semi-supervised Fuzzy Min–Max Neural Network for Data Classification

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