A novel composition forecasting model based on choquet integral with respect to L-measure and O-density

Hsiang Chuan Liu, Bai Cheng Jeng, Yen Kuei Yu, Shang Ling Ou, Yih Chang Ou, Hsien-Chang Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, based on L-measure and O-density, a novel composition forecasting model is proposed. For evaluating this new composition forecasting model, a real data experiment by using the sequential mean square error was conducted. Based on O-density, the performances of Choquet integral composition forecasting model with the L-measure, Lambda-measure and P-measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. Experimental results show that the Choquet integral composition forecasting model with respect to the L-measure outperforms other 5 composition forecasting models.

Original languageEnglish
Title of host publicationAdvanced Manufacturing Technology
Pages1245-1248
Number of pages4
Volume472-475
Publication statusPublished - 2012 Mar 21
Event3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012 - Xiamen, China
Duration: 2012 Mar 272012 Mar 29

Publication series

NameAdvanced Materials Research
Volume472-475
ISSN (Print)1022-6680

Other

Other3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012
CountryChina
CityXiamen
Period12-03-2712-03-29

Fingerprint

Chemical analysis
Linear regression
Mean square error
Experiments

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Liu, H. C., Jeng, B. C., Yu, Y. K., Ou, S. L., Ou, Y. C., & Tsai, H-C. (2012). A novel composition forecasting model based on choquet integral with respect to L-measure and O-density. In Advanced Manufacturing Technology (Vol. 472-475, pp. 1245-1248). (Advanced Materials Research; Vol. 472-475).
Liu, Hsiang Chuan ; Jeng, Bai Cheng ; Yu, Yen Kuei ; Ou, Shang Ling ; Ou, Yih Chang ; Tsai, Hsien-Chang. / A novel composition forecasting model based on choquet integral with respect to L-measure and O-density. Advanced Manufacturing Technology. Vol. 472-475 2012. pp. 1245-1248 (Advanced Materials Research).
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title = "A novel composition forecasting model based on choquet integral with respect to L-measure and O-density",
abstract = "In this paper, based on L-measure and O-density, a novel composition forecasting model is proposed. For evaluating this new composition forecasting model, a real data experiment by using the sequential mean square error was conducted. Based on O-density, the performances of Choquet integral composition forecasting model with the L-measure, Lambda-measure and P-measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. Experimental results show that the Choquet integral composition forecasting model with respect to the L-measure outperforms other 5 composition forecasting models.",
author = "Liu, {Hsiang Chuan} and Jeng, {Bai Cheng} and Yu, {Yen Kuei} and Ou, {Shang Ling} and Ou, {Yih Chang} and Hsien-Chang Tsai",
year = "2012",
month = "3",
day = "21",
language = "English",
isbn = "9783037853702",
volume = "472-475",
series = "Advanced Materials Research",
pages = "1245--1248",
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}

Liu, HC, Jeng, BC, Yu, YK, Ou, SL, Ou, YC & Tsai, H-C 2012, A novel composition forecasting model based on choquet integral with respect to L-measure and O-density. in Advanced Manufacturing Technology. vol. 472-475, Advanced Materials Research, vol. 472-475, pp. 1245-1248, 3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012, Xiamen, China, 12-03-27.

A novel composition forecasting model based on choquet integral with respect to L-measure and O-density. / Liu, Hsiang Chuan; Jeng, Bai Cheng; Yu, Yen Kuei; Ou, Shang Ling; Ou, Yih Chang; Tsai, Hsien-Chang.

Advanced Manufacturing Technology. Vol. 472-475 2012. p. 1245-1248 (Advanced Materials Research; Vol. 472-475).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - A novel composition forecasting model based on choquet integral with respect to L-measure and O-density

AU - Liu, Hsiang Chuan

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N2 - In this paper, based on L-measure and O-density, a novel composition forecasting model is proposed. For evaluating this new composition forecasting model, a real data experiment by using the sequential mean square error was conducted. Based on O-density, the performances of Choquet integral composition forecasting model with the L-measure, Lambda-measure and P-measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. Experimental results show that the Choquet integral composition forecasting model with respect to the L-measure outperforms other 5 composition forecasting models.

AB - In this paper, based on L-measure and O-density, a novel composition forecasting model is proposed. For evaluating this new composition forecasting model, a real data experiment by using the sequential mean square error was conducted. Based on O-density, the performances of Choquet integral composition forecasting model with the L-measure, Lambda-measure and P-measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. Experimental results show that the Choquet integral composition forecasting model with respect to the L-measure outperforms other 5 composition forecasting models.

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M3 - Conference contribution

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Liu HC, Jeng BC, Yu YK, Ou SL, Ou YC, Tsai H-C. A novel composition forecasting model based on choquet integral with respect to L-measure and O-density. In Advanced Manufacturing Technology. Vol. 472-475. 2012. p. 1245-1248. (Advanced Materials Research).