TY - GEN
T1 - Evolving Neural Networks with Maximum AUC for Imbalanced Data Classification
AU - LU, Xiaofen
AU - TANG, Ke
AU - YAO, Xin
PY - 2010
Y1 - 2010
N2 - Real-world classification problems usually involve imbalanced data sets. In such cases, a classifier with high classification accuracy does not necessarily imply a good classification performance for all classes. The Area Under the ROC Curve (AUC) has been recognized as a more appropriate performance indicator in such cases. Quite a few methods have been developed to design classifiers with the maximum AUC. In the context of Neural Networks (NNs), however, it is usually an approximation of AUC rather than the exact AUC itself that is maximized, because AUC is non-differentiable and cannot be directly maximized by gradient-based methods. In this paper, we propose to use evolutionary algorithms to train NNs with the maximum AUC. The proposed method employs AUC as the objective function. An evolutionary algorithm, namely the Self-adaptive Differential Evolution with Neighborhood Search (SaNSDE) algorithm, is used to optimize the weights of NNs with respect to AUC. Empirical studies on 19 binary and multi-class imbalanced data sets show that the proposed evolutionary AUC maximization (EAM) method can train NN with larger AUC than existing methods. © 2010 Springer-Verlag.
AB - Real-world classification problems usually involve imbalanced data sets. In such cases, a classifier with high classification accuracy does not necessarily imply a good classification performance for all classes. The Area Under the ROC Curve (AUC) has been recognized as a more appropriate performance indicator in such cases. Quite a few methods have been developed to design classifiers with the maximum AUC. In the context of Neural Networks (NNs), however, it is usually an approximation of AUC rather than the exact AUC itself that is maximized, because AUC is non-differentiable and cannot be directly maximized by gradient-based methods. In this paper, we propose to use evolutionary algorithms to train NNs with the maximum AUC. The proposed method employs AUC as the objective function. An evolutionary algorithm, namely the Self-adaptive Differential Evolution with Neighborhood Search (SaNSDE) algorithm, is used to optimize the weights of NNs with respect to AUC. Empirical studies on 19 binary and multi-class imbalanced data sets show that the proposed evolutionary AUC maximization (EAM) method can train NN with larger AUC than existing methods. © 2010 Springer-Verlag.
KW - AUC
KW - Class-imbalance Learning
KW - Differential Evolution
KW - Evolutionary Algorithms
KW - Feed-forward Neural Networks
KW - ROC
UR - http://www.scopus.com/inward/record.url?scp=77954581070&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13769-3_41
DO - 10.1007/978-3-642-13769-3_41
M3 - Conference paper (refereed)
SN - 9783642137686
T3 - Lecture Notes in Computer Science
SP - 335
EP - 342
BT - Hybrid Artificial Intelligent Systems, Part I : 5th International Conference, HAIS 2010, San Sebastian, Spain, June 23-25, 2010. Proceedings
A2 - ROMAY, Manuel Graña
A2 - CORCHADO, Emilio
A2 - SEBASTIAN, M. Teresa Garcia
PB - Springer Berlin Heidelberg
T2 - 5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010
Y2 - 23 June 2010 through 25 June 2010
ER -