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https://github.com/melvinmo/ropac-rule-optimized-aggregation-classifier
Python code for the ROPAC data classification algorithm
https://github.com/melvinmo/ropac-rule-optimized-aggregation-classifier
data-classification data-mining machine-learning ropac rule-based-classifier
Last synced: 4 days ago
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Python code for the ROPAC data classification algorithm
- Host: GitHub
- URL: https://github.com/melvinmo/ropac-rule-optimized-aggregation-classifier
- Owner: MelvinMo
- License: mit
- Created: 2024-04-03T15:10:01.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-04-12T20:32:06.000Z (7 months ago)
- Last Synced: 2024-04-13T04:03:04.497Z (7 months ago)
- Topics: data-classification, data-mining, machine-learning, ropac, rule-based-classifier
- Language: Python
- Homepage:
- Size: 1020 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ROPAC: **R**ule **OP**timized **A**ggregation **C**lassifier
**Authors:** [Melvin Mokhtari](https://melmo.ir/), [Alireza Basiri](https://basiri.iut.ac.ir/)
**Published in:** [Expert Systems with Applications](https://www.sciencedirect.com/journal/expert-systems-with-applications), September 2024
**Paper can be found:**
- [Directly](https://github.com/MelvinMo/ROPAC-Rule-OPtimized-Aggregation-Classifier/blob/main/ROPAC%20Rule%20OPtimized%20Aggregation%20Classifier.pdf)
- [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0957417424007632?via%3Dihub)
- [](https://doi.org/10.1016/j.eswa.2024.123897)
**Code and data can also be accessed:**
- 
- [](https://codeocean.com/capsule/2356040/tree)
- [](https://doi.org/10.24433/CO.7399708.v2)
- Locally in this GitHub repository
## Abstract
**R**ule **OP**timized **A**ggregation **C**lassifier (ROPAC) is a novel rule-based classifier that is introduced in two variants, ROPAC-L and ROPAC-M, to expand search space exploration and achieve better classification accuracy. This algorithm was evaluated on 50 diverse datasets, comparing accuracy with 15 famous algorithms, including ForestPA, LMT, MLP of Neural Networks, Random Forest, Optimized Forest, SPAARC, RACER, Bootstrap Aggregation (Bagging), C4.5, PART, the JRip implementation of RIPPER, SMO in SVM, Decision Tree (CART), IBk implementation of KNN, and Naïve Bayes. The experiments confirmed ROPAC-L as the most accurate, leading classifier.
## Citation
If you found this work helpful, please **star🌟** this repository and **cite📑** our paper. Thank you for your support!
```APA
Mokhtari, M., & Basiri, A. (2024). ROPAC: Rule OPtimized Aggregation Classifier. Expert Systems with Applications, 123897.
```
```BibTeX
@Article{Mokhtari2024,
author = {Mokhtari, Melvin and Basiri, Alireza},
title = {ROPAC: Rule OPtimized Aggregation Classifier},
year = {2024},
month = {9},
day = {15},
journal = {Expert Systems with Applications},
volume = {250},
pages = {123897},
doi = {https://doi.org/10.1016/j.eswa.2024.123897},
url = {https://www.sciencedirect.com/science/article/pii/S0957417424007632},
publisher = {Elsevier Ltd},
issn = {0957-4174},
coden = {ESAPE},
language = {English},
abbrev_source_title = {Expert Sys Appl},
type = {Article}
}
```
## Contact
If you have any questions about this repository, wish to request a feature or make a contribution, please open a [GitHub issue](https://github.com/MelvinMo/ROPAC-Rule-OPtimized-Aggregation-Classifier/issues), or feel free to contact [[email protected]](mailto:[email protected]).