Effects of Kurtosis for the Error Rate Estimators Using Resampling Methods in Two Class Discrimination

  • Authors:

  • Kozo Yamada;Hirohito Sakurai;Hideyuki Imai;Yoshiharu Sato

  • Affiliations:

  • Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814

  • Venue:
  • KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
  • Year:
  • 2009

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Abstract

In preceding studies, error rate estimators have been compared under various conditions and in most cases the population distribution was assumed to be normal. Effects of non-normality of the population have therefore not been studied sufficiently. In this study, we focused on kurtosis as a measure of non-normality and examined the effects of kurtosis for error rate estimators, especially resampling-based estimators. Our simulation results in two-class discrimination using a linear discriminant function suggest that it is necessary to consider non-normality of the population in comparison of estimators.