Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach

Shiue, Yeou-Ren; You, Gui-Rong; Su, Chao‐Ton; Chen, Hua

doi:10.1016/j.asoc.2021.107212

Cited by 16 publications

(8 citation statements)

References 41 publications

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“…As well as these advances in the machine learning area, which link well into meta-heuristic approaches, there is also scope for research into the combination of meta-heuristics with different optimisation algorithm approaches, leading to an overall more effective algorithm. In terms of algorithms research, the success of ensemble methods within the machine learning area (Abuassba et al, 2021;Shiue et al, 2021) indicates the potential of combining algorithms and models, with their diverse strengths and weaknesses, in optimisation applications (Han et al, 2020;Tóth et al, 2020;Ye et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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“…For example, the knn algorithm has good adaptability to unknown distribution data, rf has a better effect on feature loss and sample unbalanced data learning, and the deep learning model can better learn the nonlinear relationship between the data. Therefore, according to the characteristics of different algorithms, this paper selects seven commonly used algorithms as base learners, including knn, svm, rf, and gbdt used in the article [6], and also uses the naive bayes classifier (nbc) [9], linear discriminant analysis (lda) and adaptive enhancement (adaboost) [10]. The sample allocation ratio of training sets and the validation set is 5:5, and multiple sets of differentiated sample combinations are generated by cross-validation.…”

Section: Building Base Learner Selection Standardmentioning

confidence: 99%

“…Therefore, in the selective integration process, the main task is to determine the appropriate selection strategy, learner performance measures, and metrics to determine the base learner and the subset of base learners that perform well. This paper proposes a base learner subset combination determination method based on the genetic algorithm [10], and the algorithm accuracy vector acc, time cost matrix t, and diversity metric q obtained after the completion of the training of 7 base learners can be stored in the form of vector ri(i=1,2,..., 7), and finally form a matrix of r=[r1, r2,..., r7] representing the operating parameters of different base learners. Then the selection vector of the base learner is defined as sl= [1,0,...,1], where element 1 in sl indicates that the base learner is selected to construct an ensemble learning model, and 0 means that it is not selected.…”

Section: Base Learner Selection Based On Genetic Algorithmmentioning

confidence: 99%

Research on civil aircraft fault diagnosis method based on selective integration

Ren,

Ma,

Wan

et al. 2024

J. Phys.: Conf. Ser.

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Based on the research of the existing methods, this paper applies the performance parameters of the base learner and proposes the overall performance metrics of the base learner subset based on the diversity measurement method, the diagnostic accuracy of the base learner, and the running time. The performance metric as the fitness function of the genetic algorithm establishes the selection method of the subset of the base learner; the deep neural network is used to learn the complex nonlinear relationship between the base learners and a selective integration framework based on the stacking method is proposed to improve the overall diagnostic performance of integration model. Then, the data from engine operation to failure are used to verify the method performance proposed in this paper. The comparison results find that the method has a higher fault diagnosis accuracy and robustness. Finally, the method is streamlined to make it more applicable and suitable for engineering problems.

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“…After training classifiers on these subsets, the predictions are combined by majority voting. AdaBoost maintains a set of weights over the original training sets and adjusts these weights after each classifier is trained (Shiue et al, 2021). This article selects five representative techniques—over‐sampling, under‐sampling, cost‐sensitive learning, bagging, and AdaBoost—to balance the data.…”

Section: Framework For Extracting Product/service Improvement Ideasmentioning

confidence: 99%

Capturing product/service improvement ideas from social media based on lead user theory

Yin,

Jiang,

Jain

et al. 2023

J of Product Innov Manag

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Capturing valuable product/service improvement ideas is helpful for the development of new features. However, the existing methods for capturing such improvement ideas have the disadvantages of high cost, long time lag, information overload, and difficulty in getting a response. We propose an innovative framework based on lead user theory for capturing product/service improvement ideas from user‐generated content on social media (henceforth called “chatter”). To identify the chatter containing improvement ideas, we design a machine‐learning‐based imbalanced classification model. Additionally, we use text summarization technology to get a rough sense of improvement ideas from the selected chatter. We validate the proposed framework by a case study in the automotive industry. The results demonstrate that the ideas extracted by our framework are breakthrough innovative, useful, feasible, and adoptable.

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Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach

Cited by 16 publications

References 41 publications

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Research on civil aircraft fault diagnosis method based on selective integration

Capturing product/service improvement ideas from social media based on lead user theory

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