Hierarchical Classifier-Regression Ensemble for Multi-phase Non-linear Dynamic System Response Prediction: Application to Climate Analysis

Gonzalez, Doel L.; Chen, Zhengzhang; Tetteh, Isaac Kow; Pansombut, Tatdow; Semazzi, Fredrick H. M.; Kumar, Vipin; Melechko, Anatoli V.; Samatova, Nagiza F.

doi:10.1109/icdmw.2012.133

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Rather they do this using the minimum or maximum of the training data points and the bin boundary [1,12]. Also closely related to what we propose is work by Gonzalez et al for problems that involve multi-variate spatio-temporal data [11]. The main differences in our approaches is two-fold.…”

Section: Related Workmentioning

confidence: 98%

Federated Ensemble Regression Using Classification

Orhobor

Soldatova

King

2020

Lecture Notes in Computer Science

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Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case.

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Section: Related Workmentioning

confidence: 98%

Federated Ensemble Regression Using Classification

Orhobor

Soldatova

King

2020

Lecture Notes in Computer Science

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“…However, one can conjecture that if the supervised methods produce cleaner delineations between the different bins for a given response, it could improve overall predictive accuracy, as the class data on which the classifiers are built would be less noisy. There are other methods which also perform regression by means of classification in an ensemble setting-the work by Ahmad et al (2018), Halawani et al (2012) and Gonzalez et al (2012) An alternative approach to treating imbalanced data is to redress ratio of "rare" and "common" values in a dataset by strategic resampling. Inspired by a similar problem of imbalanced classes in classification, Torgo et al (2015) proposed two resampling strategies for continuous outcomes in regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced regression using regressor-classifier ensembles

et al. 2022

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We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We evaluated the extension using four classifiers and four regressors, two discretizers, and 119 responses from a wide variety of datasets in different domains. Additionally, we compared our algorithm to two resampling methods aimed at addressing imbalanced datasets. Our results show that the proposed extension is highly unlikely to perform worse than the base case, and on average outperforms the two resampling methods with significant differences in performance.

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A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events

Wang

Ding

2015

2015 IEEE International Conference on Data Mining

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Hierarchical Classifier-Regression Ensemble for Multi-phase Non-linear Dynamic System Response Prediction: Application to Climate Analysis

Cited by 3 publications

References 34 publications

Federated Ensemble Regression Using Classification

Federated Ensemble Regression Using Classification

Imbalanced regression using regressor-classifier ensembles

A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events

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