IDEAL 2011: Intelligent Data Engineering and Automated Learning - IDEAL 2011 pp 299-306 | Cite as
Iranian Cancer Patient Detection Using a New Method for Learning at Imbalanced Datasets
Abstract
Most of standard learning algorithms presume or at least expect that distributions governed on the different classes of dataset are balanced. Also they presume that the misclassification cost of each data point is equal without considering its class. These algorithms fail to learn at the imbalanced datasets. Cancer detection is a well-known domain in which it is very common to face imbalanced class distributions. This paper presents an algorithm which is suit to this field, in both speed and efficacy. The experimental results show that the performance of the proposed algorithm outperforms some of the best methods in the field.
Keywords
Imbalanced Learning Decision Tree Artificial Neural Networks Cancer DetectionPreview
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References
- 1.He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowledge And Data Engineering 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
- 2.Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory Under Sampling for Class Imbalance Learning. In: Proc. Int’l Conf. Data Mining, pp. 965–969 (2006)Google Scholar
- 3.Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory Under sampling for Class-Imbalance Learning. IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics (2009)Google Scholar
- 4.Zhang, J., Mani, I.: KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction. In: Proc. Int’l Conf. Machine Learning (ICML 2003), Workshop Learning from Imbalanced Data Sets (2003)Google Scholar
- 5.Hamzei, M., Kangavari, M.R.: Learning from imbalanced data. Technical Report. Iran University of Sci. & Tech., Iran (2010)Google Scholar
- 6.Minaei, F., Soleimanian, M., Kheirkhah, D.: Investigation the relationship between risk factors of occurrence of breast tumor in women, Aranobidgol, Iran (2009)Google Scholar
- 7.Haykin, S.: Neural Networks, a comprehensive foundation, 2nd edn. Prentice Hall International, Inc., Englewood Cliffs (1999) ISBN: 0-13-908385-5MATHGoogle Scholar
- 8.Yang, T.: Computational Verb Decision Trees. International Journal of Computational Cognition, 34–46 (2006)Google Scholar