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Dynamic ensemble selection and instantaneous pruning for regression used in signal calibration

Dias, K and Windeatt, T (2014) Dynamic ensemble selection and instantaneous pruning for regression used in signal calibration Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8681 L. pp. 475-482.

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Abstract

A dynamic method of selecting a pruned ensemble of predictors for regression problems is described. The proposed method enhances the prediction accuracy and generalization ability of pruning methods that change the order in which ensemble members are combined. Ordering heuristics attempt to combine accurate yet complementary regressors. The proposed method enhances the performance by modifying the order of aggregation through distributing the regressor selection over the entire dataset. This paper compares four static ensemble pruning approaches with the proposed dynamic method. The experimental comparison is made using MLP regressors on benchmark datasets and on an industrial application of radio frequency source calibration. © 2014 Springer International Publishing Switzerland.

Item Type: Article
Authors :
NameEmailORCID
Dias, KUNSPECIFIEDUNSPECIFIED
Windeatt, Tt.windeatt@surrey.ac.ukUNSPECIFIED
Date : 1 January 2014
Identification Number : 10.1007/978-3-319-11179-7-60
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:22
Last Modified : 17 May 2017 15:10
URI: http://epubs.surrey.ac.uk/id/eprint/838980

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