A new imbalanced learning and dictions tree method for breast cancer diagnosis

Journal of Bionanoscience

Volumn 7, Issue 6, 2013, Pages 673-678

  Parvin, Hamid ^a   Minaei Bidgoli, Behrouz ^a   Alinejad Rokny, Hamid ^b,c  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

Artificial neural networks; Breast cancer detection; Breast cancer diagnosis; Clustering; Decision tree; Imbalanced learning

Indexed keywords

EID: 84894273867     PISSN: 15577910     EISSN: 15577929     Source Type: Journal    
DOI: 10.1166/jbns.2013.1162     Document Type: Article

References (8)

1. H. He and E. A. Garcia, IEEE Trans. Knoledge and Data Engineering 21, 1263 (2009).
2. X. Y. Liu, J. Wu, and Z. H. Zhou, Exploratory under sampling for class imbalance learning, Proc. Int'l Conf. Data Mining (2006), pp. 965-969.
3. X. Y. Liu, J. Wu, and Z. H. Zhou, IEEE Transactions on Systems, Man and Sybernatics-Part B 2006, 965 (2009).
4. J. Zhang and I. Mani, KNN approach to imbalanced data distributions: A case study involving information extraction, Proc. Int'l Conf. Machine Learning, Workshop Learning from Imbalanced Data Sets, CML, Washington, DC (2003), p. 2033.
5. M. Hamzei and M. R. Kangavari, Learning from imbalanced data. Technical Report, Iran University of Sci. & Tech, Iran (2010).
6. F. Minaei, M. Soleimanian, and D. Kheirkhah, Investigation the Relationship Between Risk Factors of Occurrence of Breast Tumor in Women, Aranobidgol, Iran (2009).
7. S. Haykin, Neural Networks, A comprehensive Foundation, 2nd edn., Prentice Hall International, Inc. ISBN: 0-13-908385-5. (1999).
8. T. Yang, International Journal of Computational Cognition 4, 34 (2000).