By Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)
The two-volume set LNCS 7367 and 7368 constitutes the refereed complaints of the ninth overseas Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been conscientiously reviewed and chosen from quite a few submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; picture processing; info processing; neurocontrol; and novel applications.
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Additional info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II
0, were supplied to construct the initial population of chromosomes (marked as MF RW KNN). Table 3 show the results of comparable experiments on data sets from UCI in detail. The accuracy values shown in the table are the average value of the 10 runs. In the column of measure function ’F’, the value written in the table is the abbreviation of the measure function and the value in brackets is the number of occurrences in the 10 runs. ’ is the mean number of selected features according to the measure function which occurred most frequently in the 10 runs.
Zhao et al (2011) proposed a binary probabilistic extreme learning machine (PELM) classification method to enhance the reliability of classification and avoid the misclassification due to the uncertainty of ELM predictions . PELM uses available prior knowledge, probability density function and Bayes rules to classify the unknown samples. Parameters of probability density function are estimated by the nonlinear least squares method. However, p-DPLS and PELM are binary classification methods, which only deal with two class labels.
A reliability for each sample is calculated from each binary PELM model, and the sample is assigned to the class with the largest combined reliability by using the winner-takes-all strategy. The proposed method is verified with the operational conditions classification of an industrial wastewater treatment plant. Experimental results show the good performance on classification accuracy and computational expense. Keywords: Extreme learning machine, probabilistic extreme learning machine, Binary classification, Wastewater treatment.