Neural learning from unbalanced data

被引:62
作者
Murphey, YL [1 ]
Guo, H
Feldkamp, LA
机构
[1] Univ Michigan, Dept Elect & Comp Engn, Dearborn, MI 48128 USA
[2] Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA
关键词
machine learning; neural networks; unbalanced data; data noise;
D O I
10.1023/B:APIN.0000033632.42843.17
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes the result of our study on neural learning to solve the classification problems in which data is unbalanced and noisy. We conducted the study on three different neural network architectures, multilayered Back Propagation, Radial Basis Function, and Fuzzy ARTMAP using three different training methods, duplicating minority class examples, Snowball technique and multidimensional Gaussian modeling of data noise. Three major issues are addressed: neural learning from unbalanced data examples, neural learning from noisy data, and making intentional biased decisions. We argue that by properly generated extra training data examples around the noise densities, we can train a neural network that has a stronger capability of generalization and better control of the classification error of the trained neural network. In particular, we focus on problems that require a neural network to make favorable classification to a particular class such as classifying normal( pass)/abnormal( fail) vehicles in an assembly plant. In addition, we present three methods that quantitatively measure the noise level of a given data set. All experiments were conducted using data examples downloaded directly from test sites of an automobile assembly plant. The experimental results showed that the proposed multidimensional Gaussian noise modeling algorithm was very effective in generating extra data examples that can be used to train a neural network to make favorable decisions for the minority class and to have increased generalization capability.
引用
收藏
页码:117 / 128
页数:12
相关论文
共 16 条
[1]  
Amari S, 1996, ADV NEUR IN, V8, P176
[2]  
[Anonymous], 1986, PARALLEL DISTRIBUTED
[3]  
Carpenter G. A., 1992, IJCNN International Joint Conference on Neural Networks (Cat. No.92CH3114-6), P309, DOI 10.1109/IJCNN.1992.227156
[4]   FUZZY ARTMAP - A NEURAL NETWORK ARCHITECTURE FOR INCREMENTAL SUPERVISED LEARNING OF ANALOG MULTIDIMENSIONAL MAPS [J].
CARPENTER, GA ;
GROSSBERG, S ;
MARKUZON, N ;
REYNOLDS, JH ;
ROSEN, DB .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1992, 3 (05) :698-713
[5]   ART-2 - SELF-ORGANIZATION OF STABLE CATEGORY RECOGNITION CODES FOR ANALOG INPUT PATTERNS [J].
CARPENTER, GA ;
GROSSBERG, S .
APPLIED OPTICS, 1987, 26 (23) :4919-4930
[6]  
DAGLI CH, 1992, ARTIFICIAL NEURAL NE
[7]  
Fukunaga K., 1972, Introduction to statistical pattern recognition
[8]  
IRIE B, 1988, P IEEE INT C NEUR NE, P641
[9]  
Kosko B., 1992, NEURAL NETWORKS FUZZ
[10]  
LU Y, 1998, IEEE IJCNN