arbiter
JournalsAccount

Enhancement of New Random Forest Algorithm to Predict the Employee Attrition Rate

Rukma Ramachandran et al.

International Journal of Enterprise Network Management2025https://doi.org/10.1504/ijenm.2026.10075445article
AJG 1ABDC B
Weight
0.50

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijenm.2026.10075445

Or copy a formatted citation

@article{rukma2025,
  title        = {{Enhancement of New Random Forest Algorithm to Predict the Employee Attrition Rate}},
  author       = {Rukma Ramachandran et al.},
  journal      = {International Journal of Enterprise Network Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijenm.2026.10075445},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Enhancement of New Random Forest Algorithm to Predict the Employee Attrition Rate

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.