Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization

Zhu, Xuhui; Ni, Zhiwei; Ni, Liping; Jin, Feifei; Cheng, Ming; Wu, Zheng-Mao

doi:10.1007/s11063-020-10336-2

Cited by 4 publications

(12 citation statements)

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“…Ensemble learning encompasses the use of a collection of several classifiers whose individual decisions are combined to classify the test examples [17], [19]- [23]. It is known that an ensemble often shows a much better performance than the individual classifiers that compose it.…”

Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

“…Ensemble learning implemented by exploiting the diversification of classifiers can help in reducing the information extraction issues each single classifier might have (e.g., it would reduce the effect of overfitting when decision trees are employed). However, it is still hard to define a clear relationship between diversity and accuracy that can be achieved [19], [20], [23]. Specifically, the aforementioned methods imply that every subspace contains enough informative features for training better classifiers and increasing diversities of classifiers.…”

Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

“…Specifically, the aforementioned methods imply that every subspace contains enough informative features for training better classifiers and increasing diversities of classifiers. This is crucial to improve the performance of an ensemble classification model effectively in terms of prediction accuracy [19], [20].…”

Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

“…Transfer learning and ensemble learning are the new trends for solving the problems that arise from training data and test data having different distributions [3], [11], [17]- [19]. It has been proven that these approaches are able to capture multiple characteristics of underlying structure of data.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Marinoni,

Chlaily,

Khachatrian

et al. 2021

Preprint

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Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations. In fact, in multimodal data analysis, not all observations would show the same level of reliability or information quality, nor an homogeneous distribution of errors and uncertainties. This condition might undermine the classic assumptions ensemble learning and transfer learning methods rely on. In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue. By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified. This information is then used to set-up ensemble learning and transfer learning architectures. We test our approach on multimodal datasets acquired in diverse research fields (remote sensing, brain-computer interfaces, photovoltaic energy). Experimental results show the validity and the robustness of our approach, able to outperform state-of-the-art techniques.

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Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Marinoni,

Chlaily,

Khachatrian

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It is a non-deterministic polynomial complete problem to find the best combination of base classifiers in the ensemble classifier [30]. In recent years, the single-objective metaheuristic algorithms have been usually used for ensemble pruning to find a near-optimal solution in limited time, and the goal is generally the classification accuracy [31], [32]. Furthermore, multi-objective meta-heuristic algorithms can find a satisfactory solution in multiple performance criterias [33]- [35], and some researchers [36]- [40] have explored the application of multi-objective meta-heuristic algorithms in ensemble pruning and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Pruning of RF via Multi-Objective TLBO Algorithm and Its Parallelization on Spark

Wan¹,

Gong

Zhang

et al. 2021

IEEE Access

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Ensemble learning has been widely used in various fields. Still, too many base classifiers will affect the classification time of the ensemble classifier under the big data environment, while reducing base classifiers will affect the classification accuracy of the ensemble classifier. Therefore, the multi-objective teaching-learning-based optimization (MO-TLBO) algorithm is used to carry out ensemble pruning of random forest (RF) to improve the classification accuracy and speed of RF. MO-TLBO algorithm aims at maximizing classification accuracy and minimizing classification time, and it can find a sub-forest with higher classification accuracy and faster classification speed. In addition, considering the vast computational time of ensemble pruning of RF via MO-TLBO algorithm under the big data environment, a vote set is constructed to improve the fitness evaluation process. In the Spark platform, the RF improved by the MO-TLBO algorithm (MO-TLBO-RF) is parallelized based on data parallelism. The Shuffle optimization strategy is proposed to reduce the number of Shuffles in the execution of parallel MO-TLBO-RF. The proposed MO-TLBO-RF is applied to rolling bearing fault diagnosis. The experimental results show that the algorithm can obtain an RF with high fault diagnosis accuracy and fast fault diagnosis speed. The results also prove that the ensemble pruning time can be greatly reduced via the vote set and parallelization of MO-TLBO-RF. INDEX TERMS Ensemble pruning, multi-objective TLBO algorithm, parallelization, random forest, Spark.

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Metaheuristic-based ensemble learning: an extensive review of methods and applications

Rezk,

Selim

2024

Neural Comput & Applic

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Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.

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Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization

Cited by 4 publications

References 50 publications

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Ensemble Pruning of RF via Multi-Objective TLBO Algorithm and Its Parallelization on Spark

Metaheuristic-based ensemble learning: an extensive review of methods and applications

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