Earticle

초록

영어

The estimation of probability density function (pdf) by the nonparametric kernel methods requires a reliable estimate of the bandwidth. There have been several studies on how to efficiently estimate this parameter. In this work, we propose a new optimization method of the smoothing parameter of the variable kernel estimator (VKE) based on the statistical properties of the probability distributions of random variables. In this setting, we show how to use the maximum entropy principle for estimating the smoothing parameter. The optimized estimator is after used in building the Bayesian classifier. In the same setting, the estimated probability density function is called optimal in the sense of having a minimum error rate of classifying data. Finally, a practical implementation with the aid of a dataset of DNA microarray is used to illustrate the behavior of the optimization technique.

목차

Abstract
 1. Introduction
 2. Variable Kernel Estimator
 3. Bandwidth Selection Method
  3.1. Entropy
  3.2. Maximum Entropy Principle
  3.3. Criterion based on the Maximum Entropy Principle
  3.4. Choice of Parameter K of Variable Kernel Estimator
 4. Experimental Results
  4.1. Leukemia Dataset
  4.2. Colon Dataset
 5. Conclusion
 References

키워드

저자

• Yissam Lakhdar [ Department of Physics, Faculty of Science, University Moulay Ismail Meknes, Morocco ]
• El Hassan Sbai [ Ecole Supérieure de Technologie, University Moulay Ismail BP 3103 Route d’Agouray, 50006 Toulal, Meknes, Morocco ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605