Neural network for multi-class classification by boosting composite stumps

被引:13
作者
Nie, Qingfeng [1 ]
Jin, Lizuo [1 ,2 ]
Fei, Shumin [1 ]
Ma, Junyong [2 ]
机构
[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
[2] Sci & Technol Electroopt Control Lab, Luoyang 471009, Peoples R China
关键词
Boosting; Multi-class classification; Neural network; Composite stump; REGULARIZATION; CLASSIFIERS; SELECTION;
D O I
10.1016/j.neucom.2014.07.039
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We put forward a new model for multi-class classification problems based on the Neural Network structure. The model employs weighted linear regression for feature selection and uses boosting algorithm for ensemble learning. Unlike most previous algorithms, which need to build a collection of binary classifiers independently, the method constructs only one strong classifier once and for all classes via minimizing the total error in a forward stagewise manner. In this work, a novel weak learner framework called composite stump is proposed to improve convergence speed and share features. With these optimization techniques, the classification problem is solved by a simple but effective classifier. Experiments show that the new method outperforms the previous approaches on a number of data sets. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:949 / 956
页数:8
相关论文
共 41 条
[1]   Pattern classification of dermoscopy images: A perceptually uniform model [J].
Abbas, Qaisar ;
Celebi, M. E. ;
Serrano, Carmen ;
Fondon Garcia, Irene ;
Ma, Guangzhi .
PATTERN RECOGNITION, 2013, 46 (01) :86-97
[2]  
[Anonymous], 2005, PROC CVPR IEEE
[3]   Learning Deep Architectures for AI [J].
Bengio, Yoshua .
FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2009, 2 (01) :1-127
[4]   SRDA: An efficient algorithm for large-scale discriminant analysis [J].
Cai, Deng ;
He, Xiaofei ;
Han, Jiawei .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2008, 20 (01) :1-12
[5]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[6]   SUPPORT-VECTOR NETWORKS [J].
CORTES, C ;
VAPNIK, V .
MACHINE LEARNING, 1995, 20 (03) :273-297
[7]  
Dietterich T. G., 1995, Journal of Artificial Intelligence Research, V2, P263
[8]  
Franc V, 2002, INT C PATT RECOG, P236, DOI 10.1109/ICPR.2002.1048282
[9]  
Freund Y., 1995, Journal of computer and system sciences, P23, DOI [DOI 10.1007/3-540-59119-2_166, 10.1007/3-540-59119-2_166]
[10]  
Freund Y., 1996, INT C MACH LEARN ICM, V6, P148, DOI DOI 10.5555/3091696.3091715