基于小样本的集成学习研究

作者：倪勇1,2,吴汶芪2,李君2 日期：2009-12-30/span> 浏览：3583 查看PDF文档

基于小样本的集成学习研究

倪勇1,2,吴汶芪2,李君2
(1.浙江工业大学 信息学院，浙江 杭州 310004；2.浙江机电职业技术学院 电子信息工程系，浙江 杭州 310053)

摘要：为了提高集成学习在小数据量的有标记样本问题中的性能，结合半监督学习和选择性集成学习，提出了一种基于半监督回归的选择性集成算法SSRES。一方面，充分利用大量的未标记样本来辅助有标记样本的学习；另一方面，使用选择性集成学习进一步提高学习系统的泛化能力。实验结果表明，SSRES算法能够利用未标记样本和选择性集成技术提高学习器的性能。
关键词：集成学习；选择性集成；半监督学习；选择性集成算法
中图分类号：TP181文献标识码：A文章编号：1001-4551(2009)12-0041-04

Research of ensemble learning based on small samples
NI Yong1,2, WU Wen-qi2, LI Jun2
（1.College of Information Engineering, Zhejiang University of Technology, Hangzhou 310004, China; 2.Department of
Electronic & Information Engineering, Zhejiang Institute of Mechanical & Electric Engineering, Hangzhou 310053, China）
Abstract: In order to improve the performance of ensemble learning in a few labeled training examples, combining with semi-supervised learning and selective integration of learning, a new selective integration of algorithm based on semi-supervised regression namely SSRES was proposed. On the one hand, the method of a large number of unlabeled examples was used to reduce the requirement of labeled examples. On the other hand, selective integration learn was used to further improve the generalization ability of learning systems. Experiment results show that SSRES algorithm can improve the performance of learners with unlabeled example and selective integration of technology.
Key words: ensemble learning; selective integration; semi-supervised learning; selective integration of algorithm
参考文献(References):
［1］DIETTERICH T G. Machine-learning research: four current directions［J］. AI Magazine，1997，18 (4)：97-136.
［2］ZHU X. Semi-supervised learning literature survey［R］. Technical Report 1530, Department of Computer, University of Wisconsin at Madison, Madison, WI, Apr,2006.
［3］周志华,王珏.机器学习及其应用2006［M］.北京:清华大学出版社,2006.
［4］ZHOU Z H, WU J, TANG W. Ensembling neural networks: many could be better than all［J］.Artificial Intelligence,2002,137(1-2):239-263.
［5］TSOUMAKAS G, PARTALAS I, VLAHAVAS I. A Taxonomy and Short Review of Ensemble Selection［C］. ECAI, Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications, Patras, Greece,2008.
［6］HERNANDEZ-LOBATO D, MARTINEZ-MUNOZ G, SUAREZ A. Pruning in ordered regression bagging ensembles［C］//Proceedings of the IEEE World Congress on Computational Intelligence. Vancouver: ［s.n.］,2006:1266-1273.
［7］CHAPELLE O, ZIE A, SCHZLKOPF B. Semi-Supervised Learning［M］. MIT Press,2006.
［8］ZHOU Z H, CHEN K J, DAI H B. Enhancing relevance feedback in image retrieval using unlabeled data［J］. ACM Transactions on Information Systems,2006,24(2):219-244.
［9］ZHOU H Z, LI M. Semi-supervised learning with co-training［C］//Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI05). Edinburgh: ［s.n.］,2005:908-91.