A Median-Based Machine-Learning Approach for Predicting Random Sampling Bernoulli Distribution Parameter

Hoang Pham, David H. Pham

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In real-life applications, we often do not have population data but we can collect several samples from a large sample size of data. In this paper, we propose a median-based machine-learning approach and algorithm to predict the parameter of the Bernoulli distribution. We illustrate the proposed median approach by generating various sample datasets from Bernoulli population distribution to validate the accuracy of the proposed approach. We also analyze the effectiveness of the median methods using machine-learning techniques including correction method and logistic regression. Our results show that the median-based measure outperforms the mean measure in the applications of machine learning using sampling distribution approaches.

Original languageEnglish (US)
Pages (from-to)17-28
Number of pages12
JournalVietnam Journal of Computer Science
Volume6
Issue number1
DOIs
StatePublished - Feb 1 2019

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Software
  • Computer Science (miscellaneous)

Keywords

  • Bernoulli distribution
  • machine learning
  • median-based method
  • prediction
  • sampling distribution

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