A novel choquet integral composition forecasting model based on M-density

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

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

1 Citation (Scopus)

Abstract

In this paper, a novel density, M-density, was proposed. Based on this new density, a novel composition forecasting model was also proposed. For comparing the forecasting efficiency of this new density with the well-known density, N-density, a real data experiment was conducted. The performances of Choquet integral composition forecasting model with extensional L-measure, λ-measure and P-measure, by using M-density and N-density, 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 result showed that the Choquet integral composition forecasting model with respect to extensional L-measure based on M-density outperforms other composition forecasting models. Furthermore, for each fuzzy measure, including the L E-measure, L-measure, λ-measure and P-measure, the M-density based Choquet integral composition forecasting model is better than the N-density based.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings
Pages167-176
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2012 Mar 27
Event4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012 - Kaohsiung, Taiwan
Duration: 2012 Mar 192012 Mar 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7196 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012
CountryTaiwan
CityKaohsiung
Period12-03-1912-03-21

Fingerprint

Choquet Integral
Forecasting
Model-based
Chemical analysis
Model
Ridge Regression
Linear regression
Fuzzy Measure
Multiple Linear Regression

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Liu, H. C., Ou, S. L., Tsai, H-C., Ou, Y. C., & Yu, Y. K. (2012). A novel choquet integral composition forecasting model based on M-density. In Intelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings (PART 1 ed., pp. 167-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7196 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-28487-8_17
Liu, Hsiang Chuan ; Ou, Shang Ling ; Tsai, Hsien-Chang ; Ou, Yih Chang ; Yu, Yen Kuei. / A novel choquet integral composition forecasting model based on M-density. Intelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings. PART 1. ed. 2012. pp. 167-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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title = "A novel choquet integral composition forecasting model based on M-density",
abstract = "In this paper, a novel density, M-density, was proposed. Based on this new density, a novel composition forecasting model was also proposed. For comparing the forecasting efficiency of this new density with the well-known density, N-density, a real data experiment was conducted. The performances of Choquet integral composition forecasting model with extensional L-measure, λ-measure and P-measure, by using M-density and N-density, 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 result showed that the Choquet integral composition forecasting model with respect to extensional L-measure based on M-density outperforms other composition forecasting models. Furthermore, for each fuzzy measure, including the L E-measure, L-measure, λ-measure and P-measure, the M-density based Choquet integral composition forecasting model is better than the N-density based.",
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Liu, HC, Ou, SL, Tsai, H-C, Ou, YC & Yu, YK 2012, A novel choquet integral composition forecasting model based on M-density. in Intelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7196 LNAI, pp. 167-176, 4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012, Kaohsiung, Taiwan, 12-03-19. https://doi.org/10.1007/978-3-642-28487-8_17

A novel choquet integral composition forecasting model based on M-density. / Liu, Hsiang Chuan; Ou, Shang Ling; Tsai, Hsien-Chang; Ou, Yih Chang; Yu, Yen Kuei.

Intelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings. PART 1. ed. 2012. p. 167-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7196 LNAI, No. PART 1).

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

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N2 - In this paper, a novel density, M-density, was proposed. Based on this new density, a novel composition forecasting model was also proposed. For comparing the forecasting efficiency of this new density with the well-known density, N-density, a real data experiment was conducted. The performances of Choquet integral composition forecasting model with extensional L-measure, λ-measure and P-measure, by using M-density and N-density, 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 result showed that the Choquet integral composition forecasting model with respect to extensional L-measure based on M-density outperforms other composition forecasting models. Furthermore, for each fuzzy measure, including the L E-measure, L-measure, λ-measure and P-measure, the M-density based Choquet integral composition forecasting model is better than the N-density based.

AB - In this paper, a novel density, M-density, was proposed. Based on this new density, a novel composition forecasting model was also proposed. For comparing the forecasting efficiency of this new density with the well-known density, N-density, a real data experiment was conducted. The performances of Choquet integral composition forecasting model with extensional L-measure, λ-measure and P-measure, by using M-density and N-density, 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 result showed that the Choquet integral composition forecasting model with respect to extensional L-measure based on M-density outperforms other composition forecasting models. Furthermore, for each fuzzy measure, including the L E-measure, L-measure, λ-measure and P-measure, the M-density based Choquet integral composition forecasting model is better than the N-density based.

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Liu HC, Ou SL, Tsai H-C, Ou YC, Yu YK. A novel choquet integral composition forecasting model based on M-density. In Intelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings. PART 1 ed. 2012. p. 167-176. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-28487-8_17