一种采用LLE降维和贝叶斯分类的多类标学习算法

多类标数据中的样本可能属于一个或多个类标,因此其分类问题较单类标分类更为复杂。提出一种新的多类标学习算法,首先针对多类标数据的特征属性维数高的特点,采用LLE算法对多类标数据的特征属性进行降维,提取能较完整描述数据的一组低维特征属性集;然后将多类标样本集按所属的类标进行划分,并采用贝叶斯分类模型来学习各组样本集的分类特性;根据各个分类模型的判定类标,综合得到多类标样本的最终类标集。将该算法分别应用到自然场景图像和基因数据的多类标分类学习中,实验结果表明,该算法针对不同的多类标数据集均能取得很好的分类效果,且相比于其他多类标算法有更高的性能。

Multi-label learning by LLE dimension reduction and Bayesian classification

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.