摘要
通过近邻样例类标记确定测试样例类标记的思想在多标记分类算法中取得了良好的效果。该类算法通过对训练集进行学习,建立训练样例类标记与其k个近邻样例中不同类标记样例个数的映射关系,然后用该映射关系预测测试样例的类标记。该类算法的不足是只考虑近邻样例中不同类别样例的个数与测试样例类标记的映射关系,忽略了近邻样例与测试样例的局部相关性。考虑训练样例类与近邻样例的局部相关性,建立起它们类别间的映射关系,预测测试样例类标记,提出ML-WKNN算法。实验表明,ML-WKNN能更好地处理多标记分类问题和自动图像标注问题。
Determining the classification of the test sample by using neighbors＇ labels achieves good results in multiple label classification. The mapping relationships of these algorithms are established between the labels of training exam- ples and the number of different samples in their k-nearest neighbors by learning from the training set. The label of a test sample can be easily predicted by applying the mapping relationship. The disadvantage of these algorithms is to con- sider only the mapping relationship between the labels of the test examples and the number of different samples in their k-nearest neighbors, and to ignore the local correlation between the labels of the test examples and their k-nearest neigh- bors. This paper proposed an algorithm called ML-WKNN algorithm, which classifies the test examples through the mapping relationship between the labels of the training examples and their k-nearest neighbors by considering the local correlation between the labels of the training examples and their k-nearest neighbors. The experimental results show that the ML-WKNN algorithm achieves better results than other algorithms in dealing with the multi-label classification problems and automatic image annotation.
出处
《计算机科学》
CSCD
北大核心
2014年第2期123-126,共4页
Computer Science
基金
国家自然科学基金(61170145)
教育部高等学校博士点专项基金(20113704110001)
山东省自然科学基金
山东省科技攻关计划项目(ZR2010FM021
2010G0020115)资助
关键词
多标记学习
K近邻
分类
局部相关
Multi-label learning, KNN, Classification, Local correlation
