Bakirov, R., Gabrys, B. and Fay, D., 2016. Augmenting Adaptation with Retrospective Model Correction for Non-Stationary Regression Problems. In: International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016, Vancouver, Canada.
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Bakirov_2016_Augmenting_Adaptation_with_Retrospective_Model_Correction_for_Non-Stationary_Regression_Problems.pdf - Accepted Version
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Existing adaptive predictive methods often use multiple adaptive mechanisms as part of their coping strategy in non-stationary environments. We address a scenario when selective deployment of these adaptive mechanisms is possible. In this case, deploying each adaptive mechanism results in different candidate models, and only one of these candidates is chosen to make predictions on the subsequent data. After observing the error of each of candidate, it is possible to revert the current model to the one which had the least error. We call this strategy retrospective model correction. In this work we aim to investigate the benefits of such approach. As a vehicle for the investigation we use an adaptive ensemble method for regression in batch learning mode which employs several adaptive mechanisms to react to changes in the data. Using real world data from the process industry we show empirically that the retrospective model correction is indeed beneficial for the predictive accuracy, especially for the weaker adaptive mechanisms.
|Item Type:||Conference or Workshop Item (Speech)|
|Additional Information:||© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”|
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||17 Jan 2017 08:53|
|Last Modified:||17 Jan 2017 08:53|
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