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Cognitive Computation

Erschienen in:

01.06.2015

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

verfasst von: Chi-Man Vong, Weng-Fai Ip, Chi-Chong Chiu, Pak-Kin Wong

Erschienen in: Cognitive Computation | Ausgabe 3/2015

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Abstract

Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.

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Titel: Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine
verfasst von: Chi-Man Vong
Weng-Fai Ip
Chi-Chong Chiu
Pak-Kin Wong
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 3/2015
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-014-9301-0

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