Bayesian forecaster using class-based optimization

Jae Joon Ahn, Hyun Woo Byun, Kyong Joo Oh, Tae Yoon Kim

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Suppose that several forecasters exist for the problem in which class-wise accuracies of forecasting classifiers are important. For such a case, we propose to use a new Bayesian approach for deriving one unique forecaster out of the existing forecasters. Our Bayesian approach links the existing forecasting classifiers via class-based optimization by the aid of an evolutionary algorithm (EA). To show the usefulness of our Bayesian approach in practical situations, we have considered the case of the Korean stock market, where numerous lag-l forecasting classifiers exist for monitoring its status.

Original languageEnglish
Pages (from-to)553-563
Number of pages11
JournalApplied Intelligence
Volume36
Issue number3
DOIs
Publication statusPublished - 2012 Apr 1

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Classifiers
Evolutionary algorithms
Monitoring
Financial markets

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Ahn, Jae Joon ; Byun, Hyun Woo ; Oh, Kyong Joo ; Kim, Tae Yoon. / Bayesian forecaster using class-based optimization. In: Applied Intelligence. 2012 ; Vol. 36, No. 3. pp. 553-563.
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Bayesian forecaster using class-based optimization. / Ahn, Jae Joon; Byun, Hyun Woo; Oh, Kyong Joo; Kim, Tae Yoon.

In: Applied Intelligence, Vol. 36, No. 3, 01.04.2012, p. 553-563.

Research output: Contribution to journalArticle

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