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논문검색

Enhancing Fault Divination Accuracy Using Naïve Bayes Classifier with PYTHON and PHP

초록

영어

Programming Fault forecast has turned out to be most essential in programming Development uncommonly in programming Testing. The exact extrapolation of issues in conundrum can help to patch test effort, which decreases expenses and repair the nature of programming. Issue forecast model utilizing object situated measurements for code, datasets as info qualities to anticipate the issue probability by Naïve Bayes Classifier and these mock-ups have been far and wide utilized for bunching and grouping likewise exceptionally flawless eccentric to Bayesian systems for expansive range likelihood evaluation, generally in shortcoming expectation. In this paper, Naive Bayes classifier has been actualized on different consistent datasets.

목차

Abstract
 1. Introduction
  1.2. Classification
  1.3. Rule-Based Classification
  1.4. Decision Tree Classification
  1.5. Bayesian Classification
 2. Related Works
 3. Motivation and Uniqueness of Work
 4. Proposed Scheme
 5. Methodology
  5.1. Datasets
  5.2. Confusion Matrix:
 6. Result
 7. Conclusion and Imminent Work
 References

저자정보

  • M. Vijaya Bharathi CSE Department, GMRIT, Rajam & Research Scholar @GITAM University
  • Rodda Sireesha Department of CSE GITAM Visakhapatnam, AP, India

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