Classification of imbalanced data sets using Multi Objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Maheta:2015:ICCCI,
  author =       "Hardik H. Maheta and Vipul K. Dabhi",
  booktitle =    "2015 International Conference on Computer
                 Communication and Informatics (ICCCI)",
  title =        "Classification of imbalanced data sets using Multi
                 Objective Genetic Programming",
  year =         "2015",
  abstract =     "Classification of imbalanced data set is a challenging
                 problem as it is very difficult to achieve good
                 classification accuracy for each class in case of
                 imbalanced data sets. This problem arises in many real
                 world applications like medical diagnosis of rare
                 medical disease, fraud detection in financial domain,
                 and faulty area detection in network troubleshooting
                 etc. The imbalanced data set consists of small number
                 of instances of minority classes and large number of
                 instances of majority classes. Overall classification
                 accuracy is computed by taking the ratio of correctly
                 classified instances to total number of instances in a
                 data set. For imbalanced data sets, correct
                 classification of minority class instances contribute
                 minimum in improvement of overall classification
                 accuracy as compared to classification of majority
                 class instances. Conventional classification techniques
                 like Artificial Neural Network (ANN), Support Vector
                 Machine (SVM), and Naive Bayes (NB) consider overall
                 classification accuracy of the classifier only and thus
                 evolve biased classifiers in case of imbalanced data
                 set. However, instances of minority classes may contain
                 rare but important information in many real world data
                 sets. Thus, a classification technique that provides
                 good classification accuracy on both minority and
                 majority classes is needed. This paper proposes a
                 combination of Multi Objective Genetic Programming
                 (MOGP) and probability based Gaussian classifier for
                 classification of imbalanced data set. MOGP considers
                 classification accuracy of each class as separate
                 objective and not the overall accuracy as single
                 objective. Gaussian classifier is generative classifier
                 in which distribution of one class never affect the
                 classification of instances of other classes. The
                 proposed methodology is applied on classification of
                 imbalanced data sets from medical, life science, cars,
                 and space science domain. The results suggest that MOGP
                 classifier outperformed other conventional classifiers
                 (ANN, SVM, and NB) on tested imbalanced data sets.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICCCI.2015.7218125",
  month =        jan,
  notes =        "Also known as \cite{7218125}",
}

Genetic Programming entries for Hardik H Maheta Vipul K Dabhi

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