Least Squares Approach for Multivariate Split Selection in Regression Trees

M. Schöne, M. Kohlhase, in: C. Analide, P. Novais, D. Camacho, H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Springer International Publishing, Cham, 2020, pp. 41–50.

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Buchbeitrag | Veröffentlicht | Englisch
Herausgeber*in
Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun
Abstract
In the context of Industry 4.0, an increasing number of data-driven models is used in order to improve industrial processes. These models need to be accurate and interpretable. Regression Trees are able to fulfill these requirements, especially if their model flexibility is increased by multivariate splits that adapt to the process function. In this paper, a novel approach for multivariate split selection is presented. The direction of the split is determined by a first-order Least Squares model, that adapts to process function gradient in a local area. By using a forward selection method, the curse of dimensionality is weakened, interpretability is maintained and a generalized split is created. The approach is implemented in CART as an extension to the existing algorithm for constructing the Least Squares Regression Tree (LSRT). For evaluation, an extensive experimental analysis is performed in which LSRT leads to much smaller trees and a higher prediction accuracy than univariate CART. Furthermore, low sensitivity to noise and performance improvements for high dimensional input spaces and small data sets are achieved.
Erscheinungsjahr
Buchtitel
Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I
Seite
41-50
Konferenz
International Conference on Intelligent Data Engineering and Automated Learning
ISSN
eISSN
FH-PUB-ID

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Schöne, Marvin ; Kohlhase, Martin: Least Squares Approach for Multivariate Split Selection in Regression Trees. In: Analide, C. ; Novais, P. ; Camacho, D. ; Yin, H. (Hrsg.): Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Lecture Notes in Computer Science. Cham : Springer International Publishing, 2020, S. 41–50
Schöne M, Kohlhase M. Least Squares Approach for Multivariate Split Selection in Regression Trees. In: Analide C, Novais P, Camacho D, Yin H, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020:41-50. doi:10.1007/978-3-030-62362-3_5
Schöne, M., & Kohlhase, M. (2020). Least Squares Approach for Multivariate Split Selection in Regression Trees. In C. Analide, P. Novais, D. Camacho, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I (pp. 41–50). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-62362-3_5
@inbook{Schöne_Kohlhase_2020, place={Cham}, series={Lecture Notes in Computer Science}, title={Least Squares Approach for Multivariate Split Selection in Regression Trees}, DOI={10.1007/978-3-030-62362-3_5}, booktitle={Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I}, publisher={Springer International Publishing}, author={Schöne, Marvin and Kohlhase, Martin}, editor={Analide, Cesar and Novais, Paulo and Camacho, David and Yin, HujunEditors}, year={2020}, pages={41–50}, collection={Lecture Notes in Computer Science} }
Schöne, Marvin, and Martin Kohlhase. “Least Squares Approach for Multivariate Split Selection in Regression Trees.” In Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, edited by Cesar Analide, Paulo Novais, David Camacho, and Hujun Yin, 41–50. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-62362-3_5.
M. Schöne and M. Kohlhase, “Least Squares Approach for Multivariate Split Selection in Regression Trees,” in Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, C. Analide, P. Novais, D. Camacho, and H. Yin, Eds. Cham: Springer International Publishing, 2020, pp. 41–50.
Schöne, Marvin, and Martin Kohlhase. “Least Squares Approach for Multivariate Split Selection in Regression Trees.” Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, edited by Cesar Analide et al., Springer International Publishing, 2020, pp. 41–50, doi:10.1007/978-3-030-62362-3_5.

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