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PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods
Öz
Feature selection is a significant data mining and machine learning technique that enhances model performance by identifying important features within a dataset, reducing the risk of overfitting while aiding the model in making faster and more accurate predictions. Pyallffs is a Python library developed to optimize the feature selection process, offering rich content and low dependency requirements. With 19 different filtering methods, pyallffs assists in analyzing dataset features to determine the most relevant ones. Users can apply custom filtering methods to their datasets using pyallffs, thereby achieving faster and more effective results in data analytics and machine learning projects. The source codes, supplementary materials, and guidance is publicly available on GitHub: https://github.com/tohid-yousefi/pyallffs.
Anahtar Kelimeler
Kaynakça
- Ali Khan S, Hussain A, Basit A, Akram S. 2014. Kruskal-Wallis-based computationally efficient feature selection for face recognition. Sci World J, 2014: 1-6.
- Ali SI, Shahzad W. 2012. A feature subset selection method based on symmetric uncertainty and ant colony optimization. In: 2012 Inter Conference on Emerging Technologies, 8-9 October, 2012, Islamabad, Pakistan, pp: 1-6.
- Arauzo-Azofra A, Benitez JM, Castro JL. 2004. A feature set measure based on relief. In: Proceedings of the fifth Inter conference on Recent Advances in Soft Computing, April 27-28, Copenhagen, Denmark pp: 104-109.
- Battiti R. 1994. Using mutual information for selecting features in supervised neural net learning. IEEE Transact Neural Networks, 4: 537-550.
- Belkin M, Niyogi P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inform Proces Systems, 2001: 14.
- Beraha M, Metelli AM, Papini M, Tirinzoni A, Restelli M. 2019. Feature selection via mutual information: New theoretical insights. In: 2019 Inter Joint Conference on Neural Networks (IJCNN), 14-19 July 2019, Budapest, Hungary pp: 1-9.
- Bolón-Canedo V, Sánchez-Marono N, Alonso-Betanzos A, Benítez JM,Herrera F. 2014. A review of microarray datasets and applied feature selection methods. Inform Sci, 282: 111-135.
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Ayrıntılar
Birincil Dil
İngilizce
Konular
Bilgi Sistemleri (Diğer), Uygulamalı İstatistik, Uygulamalı Matematik (Diğer)
Bölüm
Araştırma Makalesi
Erken Görünüm Tarihi
5 Eylül 2024
Yayımlanma Tarihi
15 Eylül 2024
Gönderilme Tarihi
9 Nisan 2024
Kabul Tarihi
3 Eylül 2024
Yayımlandığı Sayı
Yıl 2024 Cilt: 7 Sayı: 5
APA
Yousefi, T., & Varlıklar, Ö. (2024). PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods. Black Sea Journal of Engineering and Science, 7(5), 971-981. https://doi.org/10.34248/bsengineering.1467132
AMA
1.Yousefi T, Varlıklar Ö. PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods. BSJ Eng. Sci. 2024;7(5):971-981. doi:10.34248/bsengineering.1467132
Chicago
Yousefi, Tohid, ve Özlem Varlıklar. 2024. “PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods”. Black Sea Journal of Engineering and Science 7 (5): 971-81. https://doi.org/10.34248/bsengineering.1467132.
EndNote
Yousefi T, Varlıklar Ö (01 Eylül 2024) PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods. Black Sea Journal of Engineering and Science 7 5 971–981.
IEEE
[1]T. Yousefi ve Ö. Varlıklar, “PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods”, BSJ Eng. Sci., c. 7, sy 5, ss. 971–981, Eyl. 2024, doi: 10.34248/bsengineering.1467132.
ISNAD
Yousefi, Tohid - Varlıklar, Özlem. “PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods”. Black Sea Journal of Engineering and Science 7/5 (01 Eylül 2024): 971-981. https://doi.org/10.34248/bsengineering.1467132.
JAMA
1.Yousefi T, Varlıklar Ö. PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods. BSJ Eng. Sci. 2024;7:971–981.
MLA
Yousefi, Tohid, ve Özlem Varlıklar. “PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods”. Black Sea Journal of Engineering and Science, c. 7, sy 5, Eylül 2024, ss. 971-8, doi:10.34248/bsengineering.1467132.
Vancouver
1.Tohid Yousefi, Özlem Varlıklar. PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods. BSJ Eng. Sci. 01 Eylül 2024;7(5):971-8. doi:10.34248/bsengineering.1467132