Financial Fraud Detection by using Grammar-based Multi-objective Genetic Programming with ensemble learning

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@InProceedings{Li:2015:CEC,
  author =       "Haibing Li and Man-Leung Wong",
  title =        "Financial Fraud Detection by using Grammar-based
                 Multi-objective Genetic Programming with ensemble
                 learning",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "1113--1120",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257014",
  abstract =     "Financial fraud is a criminal act, which violates the
                 law, rules or policy to gain unauthorized financial
                 benefit. The major consequences are loss of billions of
                 dollars each year, investor confidence or corporate
                 reputation. A study area called Financial Fraud
                 Detection (FFD) is obligatory, in order to prevent the
                 destructive results caused by financial fraud. In this
                 study, we propose a new method based on Grammar-based
                 Genetic Programming (GBGP), multi-objectives
                 optimization and ensemble learning for solving FFD
                 problems. We comprehensively compare the proposed
                 method with Logistic Regression (LR), Neural Networks
                 (NNs), Support Vector Machine (SVM), Bayesian Networks
                 (BNs), Decision Trees (DTs), AdaBoost, Bagging and
                 LogitBoost on four FFD datasets. The experimental
                 results showed the effectiveness of the new approach in
                 the given FFD problems including two real-life
                 problems. The major implications and significances of
                 the study can concretely generalize for two points.
                 First, it evaluates a number of data mining techniques
                 by the given real-life classification problems. Second,
                 it suggests a new method based on GBGP, NSGA-II and
                 ensemble learning.",
  notes =        "0945 hrs 15244 CEC2015",
}

Genetic Programming entries for Haibing Li Man Leung Wong

Citations