Ensembling a linear regression model with an error mitigation component

• Autorzy: Nowosielski A. , Kowalski P. A. , Kulczycki P.

Identyfikatory

DOI

10.14313/JAMRIS_1-2018/5

• Języki publikacji: EN

Abstrakty

EN

This paper presents a proposal of a model error mitigation technique based on the error distribution analysis of the original model and creatng the additional model that tempers the error impact in particular domain areas identified as the most sensitive. both models are then combined into single ensemble model. The idea is demonstrated on the trivial two-dimensional linear regression model.

Słowa kluczowe

EN

ensemble model; error mitigation; regression model; linear regression; FPA; RSS; RSE

PL

model regresji; regresja liniowa; FPA; RSS; RSE

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2018
• Tom: Vol. 12, No. 1
• Strony: 40--43
• Opis fizyczny: Bibliogr. 4 poz., rys.

Twórcy

autor

Nowosielski A.

• Findwise Sp. z o.o., 00-023 Warsaw, Poland, www: Findwise

autor

Kowalski P. A.

• AGH University of Science and Technology, 30-059 Cracow, Poland, Faculty of Physics and Applied Computer Science

autor

Kulczycki P.

• AGH University of Science and Technology, 30-059 Cracow, Poland, Faculty of Physics and Applied Computer Science

Bibliografia

• [1] H. Fanaee-T and J. Gama, “Event labeling combining ensemble detectors and background knowledge”,Progress in Artificial Intelligence, 2013, 1–15, 10.1007/s13748-013-0040-3.
• [2] A. Janusz, T. Tajmajer, and M. Świechowski, “Helping AI to Play Hearthstone: AAIA’17 Data Mining Challenge”. In: M. Ganzha, L. Maciaszek, and M. Paprzycki, eds., Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, vol. 11, 2017, 121–125,10.15439/2017F573.
• [3] Q. H. Vu, D. Ruta, and L. Cen, “An ensemble model with hierarchical decomposition and aggregation for highly scalable and robust classification”. In: M. Ganzha, L. Maciaszek, and M. Paprzycki, eds., Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, vol. 11,2017, 149–152, 10.15439/2017F564.
• [4] X.-S. Yang. “Flower Pollination Algorithm for Global Optimization”. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7445 LNCS, 240–249. 2012.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).