基于聚类优化的RBF神经网络多标记学习算法

J4 ›› 2012, Vol. 47 ›› Issue (5): 63-67.

基于聚类优化的RBF神经网络多标记学习算法

冯新营^1,2,计华^1,2,张化祥^1,2

1.山东师范大学信息科学与工程学院, 山东济南 250014;
2.山东省分布式计算机软件新技术重点实验室, 山东济南 250014

收稿日期:2011-11-30 出版日期:2012-05-20 发布日期:2012-06-01
作者简介:冯新营(1983- ),女,硕士研究生,主要研究方向为机器学习,数据挖掘. Email:fengxinying@sina.com
基金资助:
国家自然科学基金资助项目(61170145);山东省科技研究计划项目(2008B0026,ZR2010FM021,2010G0020115)

Multi-label RBF neural networks learning algorithm based on clustering optimization

FENG Xin-ying^1,2, JI Hua^1,2, ZHANG Hua-xiang^1,2

1. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong, China;
2. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology,
Jinan 250014, Shandong, China

Received:2011-11-30 Online:2012-05-20 Published:2012-06-01

摘要/Abstract

摘要：

多标记学习采用RBF神经网络与K-means聚类算法相结合取得了较好的效果,但由于聚类数事先不能很好地确定,无法给出准确的聚类个数值,会导致聚类质量下降、聚类结果不稳定等,进而影响RBF神经网络多标记算法的稳定性及分类性能。本文从样本几何结构的角度出发,采用一种聚类有效性指标函数,为每个类寻找最优的聚类个数,从而优化问题的求解。理论研究和实验结果表明,改进后的算法在分类的稳定性及分类性能方面都有较好的表现。

关键词: 多标记学习;RBF神经网络;K-means聚类；优化；指标函数

Abstract:

Multi-label learning, combining RBF neural network and K-means clustering algorithm, has achieved good effects. But because the number of clusters cannot be well determined in advance, an accurate value of the clustering cannot be obtained. This problem will lead to lower quality clustering and clustering instability, and then affect the stability and the classification performance of the multi-label RBF neural network algorithm. To solve the optimization problems, from the angle of sample geometry, an index function for clustering validity was employed to find the optimal number of clusters for each class. Theoretical research and experimental results show that the improved ML-IRBF algorithm can effectively boost better performance in terms of the stability and capability of classification.

Key words: multi-label learning; RBF neural network; K-means clustering; optimization; index function

冯新营1,2,计华1,2,张化祥1,2. 基于聚类优化的RBF神经网络多标记学习算法[J]. J4, 2012, 47(5): 63-67.

FENG Xin-ying1,2, JI Hua1,2, ZHANG Hua-xiang1,2. Multi-label RBF neural networks learning algorithm based on clustering optimization[J]. J4, 2012, 47(5): 63-67.

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