利用旋转森林变换的异构多分类器集成算法

doi:10.3969/j.issn.1001-2400.2014.05.009

J4 ›› 2014, Vol. 41 ›› Issue (5): 48-53.doi: 10.3969/j.issn.1001-2400.2014.05.009

利用旋转森林变换的异构多分类器集成算法

毛莎莎;熊霖;焦李成;张爽;陈博

(西安电子科技大学智能感知与图像理解教育部重点实验室，陕西西安 710071)

收稿日期:2013-07-08 出版日期:2014-10-20 发布日期:2014-11-27
通讯作者: 毛莎莎
作者简介:毛莎莎(1985-)，女，西安电子科技大学博士研究生，E-mail：skymss@126.com．
基金资助:
国家重点基础研究发展计划资助项目(2013CB329402)；国家自然科学基金资助项目(61003198,60702062)；高等学校学科创新引智计划(111计划)资助项目(B07048)

Isomerous multiple classifier ensemble via transformation of the rotating forest

MAO Shasha;XIONG Lin;JIAO Licheng;ZHANG Shuang;CHEN Bo

(Ministry of Education Key Lab. of Intelligent Perception and Image Understanding, Xidian Univ., Xi'an 710071, China)

Received:2013-07-08 Online:2014-10-20 Published:2014-11-27
Contact: MAO Shasha

摘要/Abstract

摘要：

为了增强集成系统中各分类器之间的差异性，提出了一种使用旋转森林策略集成两种不同模型分类器的方法，即异构多分类器集成学习算法．首先采用旋转森林对原始样本集进行变换划分，获得新的样本集;然后通过特定比例选择分类精度高的支撑矢量机或分类速度较快的核匹配追踪作为基本的集成个体分类器，并对新样本集进行分类，获得其预测标记；最后结合两种模型下的预测标记．该算法通过结合两种不同分类器模型，实现了精度和速度互补，将二者混合集成后改善了集成系统泛化误差，相比单个模型集成提高了系统分类性能．对UCI数据集和遥感图像数据集的仿真实验结果表明，文中算法相比单一分类器集成缩短了运行时间，同时提高了系统的分类准确率．

关键词: 集成分类器, 旋转森林, 支撑矢量机, 核匹配追踪

Abstract:

In order to boost the diversity among individual classifiers of an ensemble, a new ensemble method is proposed that combines two different classifier models via a transformation of rotation forest, named by isomerous multiple classifier ensemble．Firstly, the original samples are transformed and divided by the rotating forest to obtain new samples．Then support vector machine with the high accuracy of classification or kernel matching pursuit with the speedy classification is selected as a basic classifier model based on a special proportion, the selected classifier is used to classify the new samples, and the predictive labels are obtained. Finally, the predictive labels given by two different models are combined to obtain the final predictive labels of an ensemble. Particularly, the proposed method achieves the complementarity of accuracy and speed by combining two different classifier models, and it is important that isomerous classifier ensemble improve the generalization error of an ensemble and increases the classification performance. According to the experimental results of classification for UCI datasets and remote sensing image datasets, it is illustrated that the proposed method shortens obviously the running time and improves the accuracy of classification, compared with an ensemble based on the single classifier model.

Key words: classifier ensemble, rotation forest, support vector machine, kernel matching pursuit

中图分类号:

G623.58

毛莎莎;熊霖;焦李成;张爽;陈博. 利用旋转森林变换的异构多分类器集成算法[J]. J4, 2014, 41(5): 48-53.

MAO Shasha;XIONG Lin;JIAO Licheng;ZHANG Shuang;CHEN Bo. Isomerous multiple classifier ensemble via transformation of the rotating forest[J]. J4, 2014, 41(5): 48-53.

参考文献

［1］ Kuncheva L I. Combining Pattern Classifiers: Methods and Algorithms ［M］. New Jersey: John Wiley ＆ Sons, 2004.
［2］ Rodriguez J J, Kuncheva L I, Alonso C J. Rotation Forest: A New Classifier Ensemble Method ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(10): 1619-1630.
［3］ Zhang L, Zhou W D. Sparse Ensemble Using Weighted Combination Methods based on Linear Programming ［J］. Pattern Recognition, 2011, 44(1): 97-106.
［4］ Hansen L K, Salamon P. Neural Network Ensembles ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(10): 993-1001.
［5］吴建鑫, 周志华, 沈学华, 等. 一种选择性神经网络集成构造方法［J］. 计算机研究与发展, 2000, 37(9): 1039-1044.
Wu Jianxin, Zhou Zhihua, Shen Xuehua, et al. A Selective Constructing Approach for Neural Network Ensemble［J］. Journal of Computer Research and Development, 2000, 37(9): 1039-1044.
［6］李青, 焦李成. 利用集成支撑矢量机提高分类性能［J］. 西安电子科技大学学报, 2007, 34(1): 68-70.
Li Qing, Jiao Licheng. Improvement Classification Performance by The Support Vector Machine Ensemble［J］. Journal of Xidian University, 2007, 34(1): 68-70.
［7］ Alham N K, Li M Z, Liu Y, et al. A Distributed SVM Ensemble for Image Classification and Annotation ［C］//9th International Conference on Fuzzy Systems and Knowledge Discovery. Piscataway: IEEE, 2012: 1581-1584.
［8］ Ghorai S, Mukherjee A, Sengupta S, et al. Cancer Classification from Gene Expression Data by NPPC Ensemble ［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 8(3): 659-671.
［9］ Vapnik V. The Nature of Statistical Learning Theory ［M］. Berlin: Springer-Verlag, 1999.
［10］ Vincent P, Bengio Y. Kernel Matching Pursuit ［J］. Machine Learning, 2002, 48(1-3): 165-187.
［11］ Opitz D W. Feature Selection for Ensemble ［C］//Proceedings of the National Conference on Artificial Intelligence. New York: ACM, 1999: 379-384.
［12］ Margineantu D D, Dietterich T G. Pruning Adaptive Boosting ［C］//Proceedings of the 14th International Conference on Machine Learning. New York: ACM, 1997: 211-218.
［13］焦李成, 公茂果, 王爽, 等. 自然计算、机器学习与图像理解前沿［M］. 西安：西安电子科技大学出版社, 2008.
［14］ Breiman L. Bagging Predictors ［J］. Machine learning, 1996, 24(2): 123-140.
［15］ Tumer K, Ghosh J. Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Precancer ［J］. IEEE Transactions on Biomedical Engineering, 1998, 45(8): 953-961.
［16］ Kuncheva L I, Whitaker C J. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy ［J］. Machine Learning, 2003, 51(2): 181-207.
［17］ Valentini G, Muselli M, Ruffino F. Bagged Ensembles of Support Vector Machines for Gene Expression Data Analysis ［C］//Proceedings of the International Joint Conference on Neural Networks. Piscataway: IEEE, 2003: 1844-1849.
［18］ Yuan Hanning, Fang Meng, Zhu Xingquan. Hierarchical Sampling for Multi-Instance Ensemble Learning［J］. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2900-2905.

[1]	张姝茵;侯彪. 高概率选择和自适应MRF的极化SAR分类[J]. 西安电子科技大学学报, 2017, 44(6): 59-64.
[2]	李青;焦李成. 利用集成支撑矢量机提高分类性能 [J]. J4, 2007, 34(1): 68-70.
[3]	马建华;刘宏伟;保铮. 利用核匹配追踪算法进行雷达高分辨距离像识别[J]. J4, 2005, 32(1): 84-88.
[4]	王永刚1;2;焦李成1. 基于支撑矢量机的TD-SCDMA系统多用户检测器[J]. J4, 2004, 31(5): 744-748.
[5]	丁爱玲;刘芳;郭兰英. 基于自适应投影算法和修正核函数算法的混合支撑矢量机[J]. J4, 2002, 29(4): 477-482.
[6]	汪西莉;刘芳;焦李成. 基于大规模数据的支撑矢量机的训练和分类[J]. J4, 2002, 29(1): 123-128.
[7]	丁爱玲;刘芳;姚霞. 基于支撑矢量机的智能目标识别方法[J]. J4, 2001, 28(6): 743-747.
[8]	暂时无作者信息. 支撑矢量机的分类机理研究[J]. J4, 2000, 27(7): 106-111.

利用旋转森林变换的异构多分类器集成算法

Isomerous multiple classifier ensemble via transformation of the rotating forest

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

Metrics

本文评价

推荐阅读 0