Evolving Neural Networks with Maximum AUC for Imbalanced Data Classification

Xiaofen LU, Ke TANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

6 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems, Part I : 5th International Conference, HAIS 2010, San Sebastian, Spain, June 23-25, 2010. Proceedings
EditorsManuel Graña ROMAY, Emilio CORCHADO, M. Teresa Garcia SEBASTIAN
PublisherSpringer Berlin Heidelberg
Pages335-342
Number of pages8
ISBN (Print)9783642137686
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 - San Sebastian, Spain
Duration: 23 Jun 201025 Jun 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Berlin, Heidelberg
Volume6076
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010
Country/TerritorySpain
CitySan Sebastian
Period23/06/1025/06/10

Keywords

  • AUC
  • Class-imbalance Learning
  • Differential Evolution
  • Evolutionary Algorithms
  • Feed-forward Neural Networks
  • ROC

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