Dergiler / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2019 / Cilt: 15 - Sayı: 1

Gaussian Radial Basis Function Neural Network with Correlation Based Feature Selection Applied to Medical Text Categorization

Sayfa
75–80
Yayın Tarihi
2019-03-22
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In this context, both Correlation based Feature Filtering as a feature elimination strategy and Radial Basis Function Neural Network as text categorization algorithm are promising methods

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Text categorization is an important field for information processing systems.
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Temel Bulgular & Veri:Particularly, medical text processing is a popular research area that makes use of classification algorithms and dimension reduction strategies from machine learning field.
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Sonuç & Katkı:In this context, both Correlation based Feature Filtering as a feature elimination strategy and Radial Basis Function Neural Network as text categorization algorithm are promising methods
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:12 sample
⚙️Değişkenler:Machine learning · text categorization · neural networks

Abstract

Text categorization is an important field for information processing systems. Particularly, medical text processing is a popular research area that makes use of classification algorithms and dimension reduction strategies from machine learning field. In this study, we propose a three stage algorithm to automatically categorize medical text from OHSUMED corpus. In the proposed algorithm, we use Correlation Based Feature Filtering on top of Radial Basis Function Neural Network. The algorithm for 12 sample datasets produces 0.890 in terms macro average F-measure. In this context, both Correlation based Feature Filtering as a feature elimination strategy and Radial Basis Function Neural Network as text categorization algorithm are promising methods

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Bu Makaleye Atıf Yap

ÖZÇİFT, A. (2019). Gaussian Radial Basis Function Neural Network with Correlation Based Feature Selection Applied to Medical Text Categorization. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 15 - Sayı: 1, 75–80. https://ajindex.com/articles/100256

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Kaynakça (17)

  1. Pons, A, Gil, P, García, R, Berlanga, L. 2007. Using Typical Testors for Feature Selection in Text Categorization. Lecture Notes in Computer Science, Springer; 643-652
  2. Qirui, Z, Jinghua, T, Huaying, Z, Weiye, T, Kejing, H. Machine Learning Methods for Medical Text Categorization. Circuits, Communications and Systems, Pacific-Asia Conference, 2009, pp 494-497
  3. Yang, Y, Joachims, T. 2008. Text Categorization. Scholarpedia Text Categorization; 4242-4245
  4. Janecek, A, Gansterer, W. On the Relationship Between Feature Selection and Classification Accuracy. JMLR: Workshop and Conference Proceedings, 2009, pp 90-105
  5. Forman, G. 2007. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Resources; 1289-1305
  6. Deng, Z, Tang, S, W, Zhang, M. 2005. An Efficient Text Categorization Algorithm Based on Category Memberships. Fuzzy Systems and Knowledge Discovery; 480-485
  7. Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys; 34: 1-47
  8. Dumais, S. 1998. Using SVMs for Text Categorization. IEEE Intelligent Systems; 13: 21-23
  9. Liao, Y, Vemuri, V, R. Using Text Categorization Techniques for Intrusion Detection. Proceedings of the 11th USENIX Security Symposium, 2002, pp 51-59
  10. Li, Y, H, Jain, A, K. 1998. Classification of Text Documents. The Computer Journal; 41: 537-546
  11. Ozcift, A. 2011. Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm. Journal of Medical Systems; 1-9
  12. McNamee, P, Mayfield, J. 2004. Character N-Gram Tokenization for European Language Text Retrieval. Information Retrieval; 7: 73-97
  13. Schapire, R, Singer, Y. 2000. BoosTexter: A Boosting-based System for Text Categorization. Machine Learning; 135-168
  14. Mendez, J, Iglesias, E, Riverola, F, Diaz, F, Corchado, J. 2006. Tokenizing, Stemming and Stopword Removal on Anti-spam Filtering Domain. Current Topics in Artificial Intelligence; 449-458
  15. Text-Mining Research Group, University of West Bohemia, Influence of Word Normalization on Text Classification. http://textmining.zcu.cz/publications/inscit20060710.pdf (accessed at 10.01.2018)
  16. Lertnattee, V, Theeramunkong, T. 2007. Effects of Term Distributions on Binary Classification. IEICE Transactions on Information and Systems; 1592-1600
  17. Chou, C, Sinha, P, A, Zhao, H. 2010. A Hybrid Attribute Selection Approach for Text Classification. Journal of the Association for Information