Ensemble Pruning Based on Objection Maximization With a General Distributed Framework

Bian, Yijun; Wang, Yijun; Yao, Yaqiang; Chen, Huanhuan

doi:10.1109/tnnls.2019.2945116

Cited by 41 publications

(11 citation statements)

References 49 publications

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“…A machine learning model is considered accurate if it has a good generalization ability on unseen instances. In contrast, ML models are diverse if their errors on unseen instances are not the same [47]. Therefore, diversity is seen as the difference between base learners in an ensemble [50].…”

Section: Overview Of Ensemble Learningmentioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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Section: Overview Of Ensemble Learningmentioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…Clustering-based methods do not need a labeled pruning set as the other methods do, as it clusters the models based on a distance measurement (could be done on artificial data) and prunes the clusters until k members are left. Optimization-based ensemble pruning methods like Centralized Objection Maximization for Ensemble Pruning (COMEP) by Bian, Wang, et al starts with a single ensemble member and optimizes an objective iteratively for each new member [32]. Other methods like genetic algorithms [33] can be used as optimization-based ensemble pruners [31] as well.…”

Section: B Related Workmentioning

confidence: 99%

Engineering the Neural Automatic Passenger Counter

Jahn,

Siebert

2022

Preprint

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Automatic passenger counting (APC) in public transportation has been approached with various machine learning and artificial intelligence methods since its introduction in the 1970s. While equivalence testing is becoming more popular than difference detection (Student's t-test), the former is much more difficult to pass to ensure low user risk. On the other hand, recent developments in artificial intelligence have led to algorithms that promise much higher counting quality (lower bias). However, gradient-based methods (including Deep Learning) have one limitation: they typically run into local optima. In this work, we explore and exploit various aspects of machine learning to increase reliability, performance, and counting quality. We perform a grid search with several fundamental parameters: the selection and size of the training set, which is similar to cross-validation, and the initial network weights and randomness during the training process. Using this experiment, we show how aggregation techniques such as ensemble quantiles can reduce bias, and we give an idea of the overall spread of the results. We utilize the test success chance, a simulative metric based on the empirical distribution. We also employ a post-training Monte Carlo quantization approach and introduce cumulative summation to turn counting into a stationary method and allow unbounded counts.

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“…The credibility thresholds of the base classifiers are found by minimizing the empirical 0˘1 loss on the entire training observations. The optimization-based approach to ensemble pruning is proposed in [2], where from an information entropy perspective an objective function used in the selection process is proposed. This function takes diversity and accuracy into consideration both implicitly and simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Relearning ensemble selection based on new generated features

Burduk¹

2021

Preprint

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The ensemble methods are meta-algorithms that combine several base machine learning techniques to increase the effectiveness of the classification. Many existing committees of classifiers use the classifier selection process to determine the optimal set of base classifiers. In this article, we propose the classifiers selection framework with relearning base classifiers. Additionally, we use in the proposed framework the new generated feature, which can be obtained after the relearning process. The proposed technique was compared with state-of-the-art ensemble methods using three benchmark datasets and one synthetic dataset. Four classification performance measures are used to evaluate the proposed method.

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Ensemble Pruning Based on Objection Maximization With a General Distributed Framework

Cited by 41 publications

References 49 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Engineering the Neural Automatic Passenger Counter

Relearning ensemble selection based on new generated features

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