A self-adaptive global harmony search based extreme learning machine for classification problem

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

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Ensemble Extreme Learning Machine Based on a New Self-adaptive AdaBoost.RT

Proceedings of ELM-2014 Volume 1 - Proceedings in Adaptation, Learning and Optimization ◽

10.1007/978-3-319-14063-6_21 ◽

2015 ◽

pp. 237-244 ◽

Cited By ~ 1

Author(s):

Pengbo Zhang ◽

Zhixin Yang

Keyword(s):

Extreme Learning Machine ◽

Learning Machine ◽

Self Adaptive

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Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽

10.3390/pr7110843 ◽

2019 ◽

Vol 7 (11) ◽

pp. 843 ◽

Cited By ~ 5

Author(s):

Keke Wang ◽

Dongxiao Niu ◽

Lijie Sun ◽

Hao Zhen ◽

Jian Liu ◽

...

Keyword(s):

Wind Power ◽

Extreme Learning Machine ◽

Harmony Search ◽

Forecasting Model ◽

Short Term ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Correlation Degree ◽

Learning Machine ◽

Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

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