Ml-rbf: RBF Neural Networks for Multi-Label Learning

被引:230
作者
Zhang, Min-Ling [1 ,2 ]
机构
[1] Hohai Univ, Coll Comp & Informat Engn, Nanjing 210098, Peoples R China
[2] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210093, Peoples R China
基金
美国国家科学基金会;
关键词
Machine learning; Multi-label learning; Radial basis function; k-means clustering;
D O I
10.1007/s11063-009-9095-3
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Multi-label learning deals with the problem where each instance is associated with multiple labels simultaneously. The task of this learning paradigm is to predict the label set for each unseen instance, through analyzing training instances with known label sets. In this paper, a neural network based multi-label learning algorithm named Ml-rbf is proposed, which is derived from the traditional radial basis function (RBF) methods. Briefly, the first layer of an Ml-rbf neural network is formed by conducting clustering analysis on instances of each possible class, where the centroid of each clustered groups is regarded as the prototype vector of a basis function. After that, second layer weights of the Ml-rbf neural network are learned by minimizing a sum-of-squares error function. Specifically, information encoded in the prototype vectors corresponding to all classes are fully exploited to optimize the weights corresponding to each specific class. Experiments on three real-world multi-label data sets show that Ml-rbf achieves highly competitive performance to other well-established multi-label learning algorithms.
引用
收藏
页码:61 / 74
页数:14
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