Ensemble classifier selection using multi-objective PSO for fault diagnosis of power transformers

Peimankar, Abdolrahman; Weddell, Stephen J.; Jalal, Thahirah Syed; Lapthorn, Andrew

doi:10.1109/cec.2016.7744248

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…Over time, oversampling algorithms have become increasingly popular and are now commonly integrated into methods. In 2016, Peimankar et al [35] used a binary variant of the Multi-Objective Particle Swarm Optimization (bi-MOPSO) algorithm and adaptive synthetic (ADASYN) to handle a data imbalance, evaluating the SVM, Fuzzy k-NN, NN, Naive Bayes, and RF models. In 2017, Peimankar et al [36] proposed a two-step algorithm for power transformer fault diagnosis, exploring different architectural approaches and employing ADASYN for data imbalance.…”

Section: Related Workmentioning

confidence: 99%

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

Laburú,

Cabral,

Gomes

et al. 2024

Energies

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The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets.

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Section: Related Workmentioning

confidence: 99%

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

Laburú,

Cabral,

Gomes

et al. 2024

Energies

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“…EAs for attribute selection vary on the number of objectives to be optimized, integration with other stages, and distribution of base learners. In Peimankar et al (2016Peimankar et al ( , 2017 a multi-objective Particle Swarm Optimization algorithm provided different attribute subsets to heterogeneous base learners, in order to predict whether power transforms will fail in the near future. In Sikdar et al (2016Sikdar et al ( , 2014bSikdar et al ( , 2015, two Pareto-based multi-objective differential evolution algorithms performs attribute selection, and then linear voting weight optimization, in a pipeline fashion (the generation stage is performed before the integration stage).…”

Section: Attribute Selectionmentioning

confidence: 99%

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

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This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

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“…The fourth category of ensemble approaches incorporates different strategies for classifier selection. For example in [49] researchers used genetic algorithms to generate an ensemble classifier for unbalanced datasets; in [50], [51] researchers used multi-objective Particle Swarm Optimization (PSO) to generate an ensemble classifier. In [52], [53] researchers used PSO as a model selection tool to select the best set of classifiers to generate an ensemble classifier.…”

Section: Introductionmentioning

confidence: 99%

A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers

Jan

Verma

2019

IEEE Access

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Accuracy and diversity are considered to be the two deriving factors when it comes to generating an ensemble classifier. Focusing only on accuracy causes the ensemble classifier to suffer from ''diminishing returns'' and the ensemble accuracy tends to plateau; whereas focusing only on diversity causes the ensemble classifier to suffer in accuracy. Therefore, a balance must be maintained between the two for the ensemble classifier to achieve high classification accuracy. In this paper, we propose a novel diversity measure known as Misclassification Diversity (MD) and an Incremental Layered Classifier Selection (ILCS) approach to generate an ensemble classifier. The proposed approach ILCS-MD generates an ensemble classifier by incrementally selecting classifiers from the base classifier pool based on increasing accuracy and diversity. The benefits are in two folds 1) the generated ensemble classifier contains only those classifiers from the pool which can either maximize accuracy whilst maintaining or increasing the diversity, and 2) the generated ensemble classifier selects only a few classifiers from the base classifier pool thus reducing ensemble component size as well. The proposed approach is evaluated on 55 benchmark datasets taken from UCI and KEEL dataset repositories. The results are compared with five existing pairwise diversity measures, and existing state of the art ensemble classifier approaches. A significance test is also conducted to verify the significance of the results.INDEX TERMS Ensemble classifiers, neural networks, multiple classifiers learning, diversity measures.

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Ensemble classifier selection using multi-objective PSO for fault diagnosis of power transformers

Cited by 15 publications

References 20 publications

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

A survey of evolutionary algorithms for supervised ensemble learning

A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers

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