Predicting minority class for suspended particulate matters level by extreme learning machine

Vong, Chi‐Man; Ip, Weng Fai; Wong, Pak-Kin; Chiu, Chi-Chong

doi:10.1016/j.neucom.2012.11.056

Cited by 52 publications

(32 citation statements)

References 30 publications

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“…Deo [11] has applied this algorithm for the prediction of the Effective Drought Index in Australia, using meteorological records as inputs, and found ELM performs better than the backpropagation neural network. As one of the very few applications of ELM on air quality prediction, Vong [60] carried out an experiment on predicting the daily average level (3 classes) of P M 10 in Macau, where ELM is superior over SVM in this case.…”

Section: Air Condition Predictionmentioning

confidence: 99%

Air quality forecasting using neural networks

Zhao

Heeswijk

Karhunen

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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In this thesis project, a special type of neural network: Extreme Learning Machine (ELM) is implemented to predict the air quality based on the air quality time series itself and the external meteorological records. A regularized version of ELM with linear components is chosen to be the main model for prediction. To take full advantage of this model, its hyper-parameters are studied and optimized. Then a set of variables is selected (or constructed) to maximize the performance of ELM, where two different variable selection methods (i.e. wrapper and filtering methods) are evaluated. The wrapper method ELM-based forward selection is chosen for the variable selection. Meanwhile, a feature extraction method (Principal Component Analysis) is implemented in the hope of reducing the candidate meteorological variables for feature selection, which proves to be helpful. At last, with all the parameters being properly optimized, ELM is used for the prediction and generates satisfying results.

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Section: Air Condition Predictionmentioning

confidence: 99%

Air quality forecasting using neural networks

Zhao

Heeswijk

Karhunen

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…By designating different penalty factors for the training errors belonging to different categories, the performance of the minority classes can be highlighted. Vong, et al [9] adopted a modified random oversampling method named prior duplication to promote the recognition rate of the level of suspended particulate matter. Sun, et al [10] integrated synthetic minority oversampling technology (SMOTE) [11] into a multiple ELMs framework to improve the prediction of corporate life cycle.…”

Section: Introductionmentioning

confidence: 99%

Classification of imbalanced bioinformatics data by using boundary movement-based ELM

Chen

Yang

et al. 2015

BME

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Abstract. To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transformed space, to find the optimal moving distance of the classification hyperplane by estimating the probability density distributions of the instances in different classes. Experimental results on four real imbalanced bioinformatics classification data sets indicated that the proposed BM-ELM algorithm outperforms some traditional bias correction algorithms due to it can greatly improve the sensitivity of the classification results with small loss of specificity as possible. Also, BM-ELM algorithm has presented better performance than the widely used support vector machine (SVM) classifier. The algorithm can be widely popularized in various largescale bioinformatics applications.

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“…Among the forecasting of gaseous pollutants, the modeling of suspended particulate matters (PM 10 ) concentration was considered more challenging because of the complexity of the processes on their formation, transportation and removal of aerosol in the atmosphere [1]. An early warning system using extreme learning machine (ELM) [2] was constructed by Vong et al [3] to forecast the class of PM 10 level. Their results showed that ELM produces superior accuracy relative to support vector machine (SVM) in forecasting minority class.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, ELM has attracted the attention of many researchers in different applications, ranging from microbiology to power grid [3][4][5][6][7][8][9][10][11][12][13][14]. ELM is an improved multilayer perceptron using a more efficient training algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

Vong

Chiu

et al. 2014

Cogn Comput

Self Cite

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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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Predicting minority class for suspended particulate matters level by extreme learning machine

Cited by 52 publications

References 30 publications

Air quality forecasting using neural networks

Air quality forecasting using neural networks

Classification of imbalanced bioinformatics data by using boundary movement-based ELM

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

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