Ant colony optimization-based approach for selective neural network ensemble

被引：0
|
作者：
School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou 310015, China [1 ]
机构：
[1] School of Information and Electrical Engineering, Zhejiang University City College
来源：
Zhejiang Daxue Xuebao (Gongxue Ban) | 2009年 / 9卷 / 1568-1573期
关键词：
Ant colony optimization (ACO); Neural network; Selective ensemble;
D O I：
10.3785/j.issn.1008-973X.2009.09.004
中图分类号：
学科分类号：
摘要：
A new approach was presented to improve the performance of selective neural network ensemble by choosing the appropriate individuals that are accurate and diverse from candidate neural networks. Ant colony optimization algorithm was employed in which the selective probability depends on the pheromone and heuristic information. The pheromone is re-specified according to the accuracy of individuals while heuristic information indicates the diversity of individuals. The experiments on typical date sets show that this approach yields ensemble with smaller size while achieving much better performance, compared to the traditional Bagging and Boosting algorithm.
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页码：1568 / 1573
页数：5
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