---
title: "pavansubhasht/ibm-hr-analytics-attrition-dataset"
description: "4 papers in the tracked journals link to the Kaggle dataset pavansubhasht/ibm-hr-analytics-attrition-dataset in their Crossref records, the first in 2023; most of them (2) in 2026."
url: https://scholar4ai.com/code/kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset
markdown_url: https://scholar4ai.com/code/kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Kaggle dataset pavansubhasht/ibm-hr-analytics-attrition-dataset in their Crossref records, the first in 2023; most of them (2) in 2026. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# pavansubhasht/ibm-hr-analytics-attrition-dataset: papers linking to it

Research papers in the 480 tracked journals that link to pavansubhasht/ibm-hr-analytics-attrition-dataset (https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the Kaggle dataset pavansubhasht/ibm-hr-analytics-attrition-dataset in their Crossref records, the first in 2023; most of them (2) in 2026.
- By category: AI (3) and Economics (1); the journals with the most are Computational Economics (1), Expert Systems with Applications (1) and Neural Computing and Applications (1).

## By year

*Papers linking to pavansubhasht/ibm-hr-analytics-attrition-dataset by publication year*

| Year | Papers |
|---|---:|
| 2023 | 1 |
| 2025 | 1 |
| 2026 | 2 |

## Journals

*Journals whose papers link to pavansubhasht/ibm-hr-analytics-attrition-dataset most*

| Journal | Papers |
|---|---:|
| [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |

## Papers

*Papers linking to pavansubhasht/ibm-hr-analytics-attrition-dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-10-28 | [A modified reverse-based analysis logic mining model with Weighted Random 2 Satisfiability logic in Discrete Hopfield Neural Network and multi-objective training of Modified Niched Genetic Algorithm](https://doi.org/10.1016/j.eswa.2023.122307) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin et al. | 71 | cites it |
| 2025-07-18 | [An in-depth analysis of KernelSHAP and SamplingSHAP: assessing robustness, error, and efficiency](https://doi.org/10.1007/s10115-025-02541-z) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Beyza Aydoğan, Tevfik Aytekin | 3 | cites it |
| 2026-06-01 | [Class imbalanced safe semi-supervised learning for Customer Churn Prediction (CISL)](https://doi.org/10.1007/s00521-026-12190-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Salma Abdelmonem, Dina Elreedy, Samir I. Shaheen | 0 | cites it |
| 2026-06-06 | [Leveraging Explainable Machine Learning for Employee Attrition Prediction: Practical Implications and Job Embeddedness Insights](https://doi.org/10.1007/s10614-026-11372-1) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Dara Thomas, Ying Li, Chiagoziem C. Ukwuoma et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). pavansubhasht/ibm-hr-analytics-attrition-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset

```bibtex
@misc{scholar4ai-code-kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset,
  author = {{Scholar4AI}},
  title = {pavansubhasht/ibm-hr-analytics-attrition-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).