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.
Read or Download Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II PDF
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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
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 , 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 .
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.