Abstract
This paper presents a new approach for the cost-sensitive classification problems based on the Boosting ensemble of support vector machines (SVMs). Different from conventional Boosting ensemble learning methods that adjust the distribution of training instances for minimizing the misclassification rate, the presented approach adjusts the training data distribution so as to minimize the expected cost of classification. This approach has been applied successfully in Microcalcification (MC) detection which is a typical cost-sensitive classification problem in breast cancer diagnosis. Its performance is evaluated by means of Receiver Operating Characteristics (ROC) curves and the expected costs of classification. Experimental results have consistently confirmed that the ROC of the SVM ensemble classifier is very close to the curve enveloping the base classifier ROC curves. This characteristic illustrates that the SVM ensemble is able to always improve the performance of the classification. Furthermore, the experimental results demonstrate that the method presented is able to not only increase the area under the ROC curve (AUC) but also minimize the expected classification cost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: First International Workshop on Multiple Classifier Systems, pp. 1–15. Springer, Heidelberg (2000)
Margineantu, D., Dietterich, T.G.: Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. In: ICML 2000, pp. 583–590 (2000)
Drummond, C., Holte, R.C.: Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria. In: ICML 2000, pp. 239–246 (2000)
Ting, K.M.: A Comparative Study of Cost-Sensitive Boosting Algorithms. In: ICML 2000, pp. 983–990 (2000)
Kim, H.C., Pang, S., et al.: Pattern Classification Using Support Vector Machine Ensemble. In: ICPR 2002, pp. 20160–20163 (2002)
Kim, H.C., Peng, S., et al.: Constructing Supporting Vector Machine Ensemble. The Journal of Pattern Recognition 36, 2757–2767 (2003)
Buciu, I., Kotropoulos, C., Pitas, I.: Combining Support Vector Machines for Accurate Face Detection. In: Proc. of ICIP 2001, pp. 1054–1057 (2001)
Valentini, G., Dietterich, T.G.: Bias-variance analysis of Support Vector Machines for the Development of SVM-based Ensemble Methods. Journal of Machine Learning Research 5, 725–775 (2004)
Antonie, M.L., Zaiane, O.R., Coman, A.: Application of Data Mining Techniques for Medical Image Classification. In: MDM/KDD 2001 with ACM SIGKDD (2001)
Yu, S., Guan, L.: A CAD System for the Automatic Detection of Clustered Microcalcifications in Digitized Mammogram Films. IEEE Trans. Med. Imag. 19, 115–126 (2000)
Sajda, P., Spence, C., Pearson, J.: Learning Contextual Relationships in Mammograms Using a Hierarchical Pyramid Neural Network. IEEE Trans. Med. Imag. 21(3), 239–250 (2002)
El-Naqa, I., Yang, Y., Wernick, M.N., et al.: A Support Vector Machine Approach for Detection of Microcalcifications. IEEE Trans. Med. Imag. 21(12), 1552–1563 (2002)
Ordonez, C., Santana, C., et al.: Discovering Interesting Association Rules in Medical Data. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2000)
Zaiane, O.R., Antonie, M.L., Coman, A.: Mammography Classification by an Association Rule-based Classifier. In: MDM/KDD 2002 with ACM SIGKDD (2002)
Freund, Y., Schapire, R.E.: A Decision-theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Schapire, R.E.: A brief introduction to Boosting. In: The 16th International Joint Conference on Artificial Intelligence, pp. 1401–1406 (1999)
Elkan, C.: The Foundations of Cost-Sensitive Learning. In: The 17th International Joint Conference on Artificial Intelligence, IJCAI 2001, pp. 973–978 (2001)
Ting, K.M., Zheng, Z.: Boosting Cost-Sensitive Trees. In: The First International Conference on Discovery Science, pp. 244–255 (1998)
Fan, W., Stolfo, S., et al.: Adacost: Misclassification Cost-sensitive Boosting. In: ICML 1999, pp. 99–105 (1999)
Provost, F., Fawcett, T.: Robust Classification for Imprecise Environments. Machine Learning Journal 42(3), 203–231 (2001)
Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. Submitted to Machine Learning Journal (2004)
Bradley, A.P.: The use of the Area under the ROC curve in the Evaluation of Machine Learning Algorithms. The Journal of Pattern Recognition 30(7), 1145–1159 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peng, Y., Huang, Q., Jiang, P., Jiang, J. (2005). Cost-Sensitive Ensemble of Support Vector Machines for Effective Detection of Microcalcification in Breast Cancer Diagnosis. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_59
Download citation
DOI: https://doi.org/10.1007/11540007_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)