基于随机子空间的多标签类属特征提取算法
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摘要
目前多标签学习已广泛应用到很多场景中。在此类学习问题中,一个样本往往可以同时拥有多个类别标签。因为类别标签可能带有的特有属性(即类属属性)将更有助于标签分类,所以已经出现了一些基于类属属性的多标签学习算法。针对类属属性构造会导致属性空间存在冗余的问题,提出了一种多标签类属特征提取算法LIFT_RSM。该算法基于类属属性空间通过综合利用随机子空间模型及成对约束降维思想提取有效的特征信息,以达到提升分类性能的目的。在多个数据集上的实验结果表明,与若干经典的多标签算法相比,提出的LIFT_RSM算法能得到更好的分类效果。
Multi-label learning has been widely used in many application scenarios right now. In this kind of learning problem,each instance is simultaneously assigned with more than one class label. Since different class labels might had their own unique characteristics(such as label-specific feature) which would be more useful for label classification,so some multi-label learning approaches based on label-specific features had already been proposed. Therefore,aiming at the problem that redundant feature space caused by label-specific feature construction,this paper proposed a multi-label label-specific feature extraction algorithm named LIFT_RSM,which could improve the performance of classification by comprehensively using random subspace method and the thought of pair-wise constraint dimensionality reduction to extract effective feature information in labelspecific feature space. The experimental results on several datasets show that the proposed algorithm can achieve better classification results compared with several classical multi-label algorithms.
Multi-label learning has been widely used in many application scenarios right now. In this kind of learning problem,each instance is simultaneously assigned with more than one class label. Since different class labels might had their own unique characteristics(such as label-specific feature) which would be more useful for label classification,so some multi-label learning approaches based on label-specific features had already been proposed. Therefore,aiming at the problem that redundant feature space caused by label-specific feature construction,this paper proposed a multi-label label-specific feature extraction algorithm named LIFT_RSM,which could improve the performance of classification by comprehensively using random subspace method and the thought of pair-wise constraint dimensionality reduction to extract effective feature information in labelspecific feature space. The experimental results on several datasets show that the proposed algorithm can achieve better classification results compared with several classical multi-label algorithms.
引文
[1] Zhang Minling,Zhou Zhihua. A review on multi-label learning algorithms[J]. IEEE Trans on Knowledge&Data Engineering,2014,26(8):1819-1837.
[2] Tsoumakas G,Katakis I. Multi-label classification:an overview[J].International Journal of Data Warehousing&Mining,2009,3(3):1-13.
[3] Zhou Zhihua,Zhang Minling. Multi-label learning[M]//Encyclopedia of Machine Learning and Data Mining. Berlin:Springer,2017:875-881.
[4] Zhang Minling,Zhang Kun. Multi-label learning by exploiting label dependency[C]//Proc of the 16 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press,2010:999-1008.
[5] Fürnkranz J,Hüllermeier E,Mencía E L,et al. Multilabel classification via calibrated label ranking[J]. Machine Learning,2014,73(2):133-153.
[6] Boutell M R,Luo Jiebo,Shen Xipeng,et al. Learning multi-label scene classification.[J]. Pattern Recognition,2004,37(9):1757-1771.
[7] Zhang Minling,Zhou Zhihua. ML-KNN:a lazy learning approach to multi-label learning[J]. Pattern Recognition,2007,40(7):2038-2048.
[8] Zhang Minling,Zhou Zhihua. Multilabel neural networks with applications to functional genomics and text categorization[J]. IEEE Trans on Knowledge and Data Engineering,2006,18(10):1338-1351.
[9] Schapire R E,Singer Y. BoosTexter:a boosting-based system for text categorization[J]. Machine Learning,2000,39(2-3):135-168.
[10]Tsoumakas G,Vlahavas I. Random K-labelsets:an ensemble method for multilabel classification[C]//Proc of the 18th European Conference on Machine Learning. Berlin:Springer,2007:406-417.
[11]Read J,Pfahringer B,Holmes G,et al. Classifier chains for multi-label classification[J]. Machine Learning,2011,85(3):254-269.
[12]Zhang Minling,Wu Lei. Lift:multi-label learning with label-specific features[J]. IEEE Trans on Pattern Analysis&Machine Intelligence,2015,37(1):107-120.
[13]Klein D,Kamvar S D,Manning C D. From instance-level constraints to space-level constraints:making the most of prior knowledge in data clustering[C]//Proc of the 19th International Conference on Machine Learning. San Francisco:Morgan Kaufmann Publishers Inc,2002:307-314.
[14]Ho T K. Random decision forests[C]//Proc of the 3rd International Conference on Document Analysis and Recognition. Piscatwawy,NJ:IEEE Press,2002:278.
[15]Ho T K. The random subspace method for constructing decision forests[J]. IEEE Trans on Pattern Analysis&Machine Intelligence,1998,20(8):832-844.
[16]刘建伟,刘媛,罗雄麟.半监督学习方法[J].计算机学报,2015,38(8):1592-1617.(Liu Jianwei,Liu Yuan,Luo Xionglin.Semi-supervised learning methods[J]. Chinese Journal of Computers,2015,38(8):1592-1617.)
[17]Godbole S,Sarawagi S. Discriminative methods for multi-labeled classification[C]//Proc of Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin:Springer,2004:22-30.
[18]Yang Yiming. An evaluation of statistical approaches to text categorization[J]. Information Retrieval Journal,1999,1(1):69-90.
[19]Zhang Yin,Zhou Zhihua. Multilabel dimensionality reduction via dependence maximization[J]. ACM Trans on Knowledge Discovery from Data,2010,4(3):1503-1505.
[20]Zhang Minling,Pe1a J M,Robles V. Feature selection for multi-label naive Bayes classification[J]. Information Sciences,2009,179(19):3218-3229.
[21]Yu Kai,Yu Shipeng,Tresp V. Multi-label informed latent semantic indexing[C]//Proc of International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press,2005:258-265.
[2] Tsoumakas G,Katakis I. Multi-label classification:an overview[J].International Journal of Data Warehousing&Mining,2009,3(3):1-13.
[3] Zhou Zhihua,Zhang Minling. Multi-label learning[M]//Encyclopedia of Machine Learning and Data Mining. Berlin:Springer,2017:875-881.
[4] Zhang Minling,Zhang Kun. Multi-label learning by exploiting label dependency[C]//Proc of the 16 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press,2010:999-1008.
[5] Fürnkranz J,Hüllermeier E,Mencía E L,et al. Multilabel classification via calibrated label ranking[J]. Machine Learning,2014,73(2):133-153.
[6] Boutell M R,Luo Jiebo,Shen Xipeng,et al. Learning multi-label scene classification.[J]. Pattern Recognition,2004,37(9):1757-1771.
[7] Zhang Minling,Zhou Zhihua. ML-KNN:a lazy learning approach to multi-label learning[J]. Pattern Recognition,2007,40(7):2038-2048.
[8] Zhang Minling,Zhou Zhihua. Multilabel neural networks with applications to functional genomics and text categorization[J]. IEEE Trans on Knowledge and Data Engineering,2006,18(10):1338-1351.
[9] Schapire R E,Singer Y. BoosTexter:a boosting-based system for text categorization[J]. Machine Learning,2000,39(2-3):135-168.
[10]Tsoumakas G,Vlahavas I. Random K-labelsets:an ensemble method for multilabel classification[C]//Proc of the 18th European Conference on Machine Learning. Berlin:Springer,2007:406-417.
[11]Read J,Pfahringer B,Holmes G,et al. Classifier chains for multi-label classification[J]. Machine Learning,2011,85(3):254-269.
[12]Zhang Minling,Wu Lei. Lift:multi-label learning with label-specific features[J]. IEEE Trans on Pattern Analysis&Machine Intelligence,2015,37(1):107-120.
[13]Klein D,Kamvar S D,Manning C D. From instance-level constraints to space-level constraints:making the most of prior knowledge in data clustering[C]//Proc of the 19th International Conference on Machine Learning. San Francisco:Morgan Kaufmann Publishers Inc,2002:307-314.
[14]Ho T K. Random decision forests[C]//Proc of the 3rd International Conference on Document Analysis and Recognition. Piscatwawy,NJ:IEEE Press,2002:278.
[15]Ho T K. The random subspace method for constructing decision forests[J]. IEEE Trans on Pattern Analysis&Machine Intelligence,1998,20(8):832-844.
[16]刘建伟,刘媛,罗雄麟.半监督学习方法[J].计算机学报,2015,38(8):1592-1617.(Liu Jianwei,Liu Yuan,Luo Xionglin.Semi-supervised learning methods[J]. Chinese Journal of Computers,2015,38(8):1592-1617.)
[17]Godbole S,Sarawagi S. Discriminative methods for multi-labeled classification[C]//Proc of Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin:Springer,2004:22-30.
[18]Yang Yiming. An evaluation of statistical approaches to text categorization[J]. Information Retrieval Journal,1999,1(1):69-90.
[19]Zhang Yin,Zhou Zhihua. Multilabel dimensionality reduction via dependence maximization[J]. ACM Trans on Knowledge Discovery from Data,2010,4(3):1503-1505.
[20]Zhang Minling,Pe1a J M,Robles V. Feature selection for multi-label naive Bayes classification[J]. Information Sciences,2009,179(19):3218-3229.
[21]Yu Kai,Yu Shipeng,Tresp V. Multi-label informed latent semantic indexing[C]//Proc of International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press,2005:258-265.
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