Bayesian forecaster using class-based optimization

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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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
Issue number3
Publication statusPublished - 2012 Apr

Bibliographical note

Funding Information:
Acknowledgements T.Y. Kim’s work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2009-0065645).

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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