Novel Approach of programming effort estimation by boosting and bagging approach with feature selection using optimization.

  • Unique Paper ID: 146827
  • PageNo: 114-117
  • Abstract:
  • In this theory highlight set is expanded by including Line of code (LOC). By utilizing these highlights exertion data is enhanced and help in programming improvement. In second part includes are chosen by utilizing Gray wolf streamlining (GWO) calculation. In the two works arbitrary woodland and examining with boosting and stowing technique is utilized which enhances the irregular backwoods preparing model. In this work Random woods with GWO and Random backwoods without GWO is contrasted and parameter Accuracy, Precision and Recall.
add_icon3email to a friend

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{146827,
        author = {INDU BALA and Gagan Dhawan},
        title = {Novel Approach of programming effort estimation by boosting and bagging approach  with feature selection using optimization.},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {2},
        pages = {114-117},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146827},
        abstract = {In this theory highlight set is expanded by including Line of code (LOC). By utilizing these highlights exertion data is enhanced and help in programming improvement. In second part includes are chosen by utilizing Gray wolf streamlining (GWO) calculation. In the two works arbitrary woodland and examining with boosting and stowing technique is utilized which enhances the irregular backwoods preparing model. In this work Random woods with GWO and Random backwoods without GWO is contrasted and parameter Accuracy, Precision and Recall.},
        keywords = {random forest ,optimization, bagging},
        month = {},
        }

Cite This Article

BALA, I., & Dhawan, G. (). Novel Approach of programming effort estimation by boosting and bagging approach with feature selection using optimization.. International Journal of Innovative Research in Technology (IJIRT), 5(2), 114–117.

Related Articles