Advances in Neural Networks – ISNN 2012: 9th International by Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang,

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 lawsuits of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers provided 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; development reputation; imaginative and prescient; picture processing; info processing; neurocontrol; and novel applications.

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Extra info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II

Example text

Extreme learning machine has been effectively used in regression and classification problems. Though ELM tends to provide better generalization performance at a fast learning speed and relative simplicity of use [12], ELM algorithm may have uncertainty in different trials of prediction due to the stochastic initialization of input weights and bias, which would make the classification of the raw data unreliable. Pérez et al. (2009) proposed probabilistic discriminant partial least squares (p-DPLS) to improve the reliability of the classification by integrating density methods and Bayes decision theory [13].

It overcomes the weakness of using a fixed measure function and equally-significant feature weights in the conventional KNN classifier. Three sets of carefully designed experiments indicate that the proposed approach has a good effect on improving the classification rate and removing the irrelevant or redundant features. We take the classification rate as the fitness value in the implementation of the proposed approach. In future research we will incorporate multiple criteria, such as the length of selected features and the computation cost of measure function.

Is the mean number of selected features according to the measure function which occurred most frequently in the 10 runs. The number of selected features is calculated as following: for the binary weights, if the bit gets the value 1 more than half of all the resulting chromosomes of 10 runs , then we judge the feature corresponding to this bit is selected. 0, then the feature is counted. From the experimental results shown in the Table 3, we can find some overall trends. From the point of view of the classification rate, the classifiers with feature selection, BW KNN, RW KNN, and the classifiers with measure function selection and feature selection, MF BW KNN, MF RW KNN consistently improved by a small margin over the classifier NKNN on most data sets.

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