Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm

Authors:
Piyasak Jeatrakul;Kok Wai Wong;Chun Che Fung
Affiliations:
School of Information Technology, Murdoch University, Murdoch, Western Australia;School of Information Technology, Murdoch University, Murdoch, Western Australia;School of Information Technology, Murdoch University, Murdoch, Western Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 7
Cited 1

Balancing Bias and Variance: Network Topology and Pattern Set Reduction Techniques

IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Improving Identification of Difficult Small Classes by Balancing Class Distribution

AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
A study of the behavior of several methods for balancing machine learning training data

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining on Imbalanced Data Sets

ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
Porosity Prediction Using Bagging of Complementary Neural Networks

ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Binary classification using ensemble neural networks and interval neutrosophic sets

Neurocomputing
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research

Editorial: Parameter-free classification in multi-class imbalanced data sets

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic Minority Over-sampling Technique (SMOTE) and Complementary Neural Network (CMTNN) to handle the problem of classifying imbalanced data. In order to demonstrate that the proposed technique can assist classification of imbalanced data, several classification algorithms have been used. They are Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). The benchmark data sets with various ratios between the minority class and the majority class are obtained from the University of California Irvine (UCI) machine learning repository. The results show that the proposed combination techniques can improve the performance for the class imbalance problem.