一种采用LLE降维和贝叶斯分类的多类标学习算法

Abstract

Abstract: Samples of multi-label data may belong to more than one class,so its classification problem is much more complicated than single-label data.A novel multi-label learning algorithm is proposed.The feature attributes of multi-label data often have high dimensions,so an LLE algorithm is applied to decrease the dimension in order to extract a group of low dimensional feature attribute sets which could completely describe data.Then multi-label samples are partitioned in terms of their belonging classes,and the classification characteristics of each group are learned by using Bayesian classification model.After that,the final class-label set of multi-label samples is obtained according to the decision class-label of each classification model.The algorithm is applied to the multi-label classification learning of both nature scene image and gene data respectively.Experimental results show that the proposed algorithm can acquire good classification effects on different multi-label datasets and has better performance compared with the others.

CLC Number:

TP391.4

LI Hong, XIE Zheng, XIANG Yao, WU Min. Multi-label learning by LLE dimension reduction and Bayesian classification[J]. Journal of Systems Engineering and Electronics, 2009, 31(6): 1467-1472.

References

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Multi-label learning by LLE dimension reduction and Bayesian classification

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