|Table of Contents|

Extreme Learning Machine with Initialized ParametersBased on External Class Invasion Degree(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年03期
Page:
53-
Research Field:
计算机工程
Publishing date:

Info

Title:
Extreme Learning Machine with Initialized ParametersBased on External Class Invasion Degree
Author(s):
Kong Shuangshuang1; 2; Wang Kaijun1; 2; Lin Song1; 2
(1.School of Mathematics and Information,Fujian Normal University,Fuzhou 350117,China)(2.Digital Fujian Environmental Monitoring Internet of Things Laboratory,Fujian Normal University,Fuzhou 350117,China)
Keywords:
extreme learning machine; overlapping area; external class invasion degree; weight correction
PACS:
TP183
DOI:
10.3969/j.issn.1672-1292.2019.03.008
Abstract:
Aiming at the problem that the random initialization of the input weights in the classical extreme learning machine which is easy to cause the output stability is not good enough and then affects the classification performance,an extreme learning machine with initialized parameters based on external class invasion degree is proposed by adjusting the weights initialized randomly of the extreme learning machine through the attribute characteristic information of samples. For the data set containing samples of two different categories,the method takes one class as this class and the other class as outer class. For each feature,the proportion of the overlapping areas between the samples of this class and outer class in the value range of this class is calculated,and the proportion of outer class samples in the total number of samples in the overlapping areas is also calculated. Then the invasion degree of each feature according to the two proportions is calculated. Finally,the input weights of the hidden layer of the extreme learning machine model is adjusted according to the degree of invasion. Experimental results on ten UCI data sets show that the accuracy of the new method is 1%～23% higher than that of the classical extreme learning machine,and the generalization performance is more stable; and that compared with other two methods,the accuracy of the new method is slightly higher.

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Last Update: 2019-09-30