Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30831
Feature Selection for Breast Cancer Diagnosis: A Case-Based Wrapper Approach

Authors: Mahdi Hosseini, Mohammad Darzi, Ali AsgharLiaei, HabibollahAsghari


This article addresses feature selection for breast cancer diagnosis. The present process contains a wrapper approach based on Genetic Algorithm (GA) and case-based reasoning (CBR). GA is used for searching the problem space to find all of the possible subsets of features and CBR is employed to estimate the evaluation result of each subset. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer (WDBC) dataset.

Keywords: Case-based Reasoning, Genetic Algorithm, breast cancer diagnosis, Wrapper feature selection

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2355


[1] I. Harirchi, et al., "Breast cancer in Iran: a review of 903 case records," Public Health, 2000. 114(2): p. 143-145.
[2] T. Subashini, V. Ramalingam, and S. Palanivel, "Breast mass classification based on cytological patterns using RBFNN and SVM," Expert Systems with Applications, 2009. 36(3): p. 5284-5290.
[3] R.A. Miller, "Medical diagnostic decision support systems - past, present, and future," Journal of the American Medical Informatics Association, 1994. 1(1): p. 8.
[4] J. Han, and M. Kamber, "Data mining: concepts and techniques," 2006: Morgan Kaufmann.
[5] R. Kohavi, and G.H. John, "Wrappers for feature subset selection," Artificial intelligence, 1997. 97(1-2): p. 273-324.
[6] Y. Yuling, "A Feature Selection Method for Online Hybrid Data Based on Fuzzy-rough Techniques," 2009: IEEE.
[7] N. Abe, et al., "A divergence criterion for classifier-independent feature selection," Advances in Pattern Recognition, 2000: p. 668-676.
[8] M. Dash, and H. Liu, "Feature selection for classification," Intelligent data analysis, 1997. 1(3): p. 131-156.
[9] R. Jensen, and Q. Shen, "Computational intelligence and feature selection: rough and fuzzy approaches," IEEE Press Series On Computational Intelligence, 2008: p. 340.
[10] I. Guyon, and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, 2003. 3: p. 1157- 1182.
[11] M. Sun, et al. "A GA-Based Feature Selection for High-Dimensional Data Clustering," 2009: IEEE.
[12] C.H. Yang, et al., "A Novel GA-Taguchi-Based Feature Selection Method," Intelligent Data Engineering and Automated Learning-IDEAL 2008, 2008: p. 112-119.
[13] I.S. Oh, J.S. Lee, and B.R. Moon, "Hybrid genetic algorithms for feature selection," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004: p. 1424-1437.
[14] P. Zhang, B. Verma, and K. Kumar, "Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection," Pattern Recognition Letters, 2005. 26(7): p. 909-919.
[15] J.H. Hong, and S.B. Cho, "Efficient huge-scale feature selection with speciated genetic algorithm," Pattern Recognition Letters, 2006. 27(2): p. 143-150.
[16] R. Leardi, and A. Lupiáñez González, "Genetic algorithms applied to feature selection in PLS regression: how and when to use them," Chemometrics and Intelligent Laboratory Systems, 1998. 41(2): p. 195- 207.
[17] M., Mitchell, "An introduction to genetic algorithms," 1998: The MIT press.
[18] J.H. Holland, "Adaptation in natural and artificial systems," 1992: MIT Press Cambridge, MA, USA.
[19] A. Aamodt, and E. Plaza, "Case-based reasoning: Foundational issues, methodological variations, and system approaches," AI communications, 1994. 7(1): p. 39-59.
[20] H. Ahn, K. Kim, and I. Han, "A case-based reasoning system with the two-dimensional reduction technique for customer classification," Expert Systems with Applications, 2007. 32(4): p. 1011-1019.
[21] H. Ahn, K. Kim, and I. Han, "Hybrid genetic algorithms and case based reasoning systems for customer classification," Expert Systems, 2006. 23(3): p. 127-144.
[22] H. Ahn, and K. Kim, "Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach," Applied Soft Computing, 2009. 9(2): p. 599-607.
[23] K.J. Kim, "Toward global optimization of case-based reasoning systems for financial forecasting," Applied Intelligence, 2004. 21(3): p. 239-249.
[24] G.R. Beddoe, and S. Petrovic, "Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering," European Journal of Operational Research, 2006. 175(2): p. 649-671.
[25] J. Jarmulak, S. Craw, and R. Rowe, "Genetic algorithms to optimise CBR retrieval," Advances in Case-Based Reasoning, 2000: p. 159-194.
[26] E. Golobardes, X. Llor , and M. Salam├│, "Computer aided diagnosis with case-based reasoning and genetic algorithms," Knowledge-Based Systems, 2002. 15(1-2): p. 45-52.
[27] Y. Avramenko, and A. Kraslawski, Case Based Design. Applications in Process Engineering, 2008: p. 51-108.
[28] M. Bacauskiene, and A. Verikas, "Selecting salient features for classification based on neural network committees," Pattern Recognition Letters, 2004. 25(16): p. 1879-1891.
[29] Y. Prasad, K. Biswas, and C. Jain, "SVM Classifier Based Feature Selection Using GA, ACO and PSO for siRNA Design," Advances in Swarm Intelligence, 2010: p. 307-314.