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Binary Classification of Objects with Nominal Indicators

Author

Listed:
  • Goryainova, E.

    (National Research University - Higher School of Economics, Moscow, Russia)

  • Slepneva, T.

    (Moscow Aviation Institute, Moscow, Russia)

Abstract

In this work a problem is studied of classification of respondents into classes accepting and not participation in a charity actions. An optimal (in Bayes sense) decisive discriminant rule of division of objects on two classes is constructed for the case when all indicators of observable objects are measured in a nominal scale, and there are signs of dependence between them . Using ROC-analysis methods, comparison of the developed rule with a rule implemented in the software package SPSS (Fisher’s discriminant rule), «naive» Bayesian classifier, a rule based on support vector machines (SVM) method and implemented in SPSS package binary logistic regression classifier is made. Results of the ROC-analysis have shown that the proposed rule has higher quality than all other mentioned rules of classification of respondents.

Suggested Citation

  • Goryainova, E. & Slepneva, T., 2012. "Binary Classification of Objects with Nominal Indicators," Journal of the New Economic Association, New Economic Association, vol. 14(2), pages 27-49.
  • Handle: RePEc:nea:journl:y:2012:i:14:p:27-49
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    More about this item

    Keywords

    discriminant analysis; solving rule; Bayes solution; Fisher’s linear rule; binary logistic regression; support vector machines method; ROC-curve; AUC indicator;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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