Ranking evaluation functions to improve genetic feature selection in content-based image retrieval of mammograms

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


S.F. Silva, A.J.M. Traina, M.X. Ribeiro, J.E.S. Batista-Neto, C. Traina Jr.: Ranking evaluation functions to improve genetic feature selection in content-based image retrieval of mammograms. 22nd IEEE International Symposium on Computer- Based Medical Systems, CBMS, 2009, pp. 1–8.




The ranking problem is a crucial task in the information retrieval systems. In this paper, we take advantage of single valued ranking evaluation functions in order to develop a new method of genetic feature selection tailored to improve the accuracy of content-based image retrieval systems. We propose to boost the feature selection ability of the genetic algorithms (GA) by employing an evaluation criteria (fitness function) that relies on order-based ranking evaluation functions. The evaluation criteria are provided by the GA and has been successfully employed as a measure to evaluate the efficacy of content-based image retrieval process, improving up to 22% the precision of the query answers. Experiments on three medical datasets containing breast cancer diagnosis and breast tissue density analysis showed that fitness functions based on ranking evaluation functions occupy an essential role on the algorithms' performance, obtaining results significatively better than other fitness function designs. The experiments also showed that the proposed method obtains results superior than feature selection based on the traditional decision-tree C4.5, naive bayes, support vector machine, 1-nearest neighbor and association rule mining.

Extended Abstract


author={S. F. da Silva and A. J. M. Traina and M. X. Ribeiro and J. do E. S. Batista Neto and C. Traina},
booktitle={Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on},
title={Ranking evaluation functions to improve genetic feature selection in content-based image retrieval of mammograms},
keywords={biological organs;cancer;diagnostic radiography;feature extraction;genetic algorithms;image retrieval;mammography;medical image processing;tumours;breast cancer diagnosis;breast tissue density analysis;content-based image retrieval;fitness function;genetic algorithm;genetic feature selection;mammograms;ranking evaluation function;Algorithm design and analysis;Biomedical imaging;Breast cancer;Breast tissue;Content based retrieval;Genetic algorithms;Image retrieval;Information retrieval;Medical diagnostic imaging;Performance analysis},
url={http://dx.doi.org/10.1109/CBMS.2009.5255397   http://de.evo-art.org/index.php?title=Ranking_evaluation_functions_to_improve_genetic_feature_selection_in_content-based_image_retrieval_of_mammograms},

Used References

P. M. d. Azevedo-Marques, N. A. Rosa, A. J. M. Traina, C. Traina-Jr., S. K. Kinoshita, and R. M. Rangayyan. Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge. International Journal of Computer Assisted Radiology and Surgery, 3(1-2): 123-130, June 2008. http://dx.doi.org/10.1007/s11548-008-0154-4

R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, Essex, UK, 1999.

B. Bartell, G. Cottrell, and R. Belew. Optimizing similarity using multi-query relevance. Journal of the American Society for Information Science, 49:742-761, 1998. (Pubitemid 128572075) http://dx.doi.org/10.1002/(SICI)1097-4571(199806)49:8<742::AID-ASI8>3.0.CO;2-H

O. Cordón, E. Herrera-Viedma, C. López-Puljalte, M. Luque, and C. Zarco. A review on the application of evolutionary computation to information retrieval. International Journal of Approximate Reasoning, 34:241-264, July 2003. http://dx.doi.org/10.1016/j.ijar.2003.07.010

J. G. Dy, C. E. Brodley, A. Kak, L. S. Broderick, and A. M. Aisen. Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(3):373378, March 2003. http://dx.doi.org/10.1109/TPAMI.2003.1182100

W. Fan, E. A. Fox, P. Pathak, and H. Wu. The effects of fitness functions on genetic programming-based ranking discovery for web search. Journal of the American Society for Information Science and Technology, 55(7):628-636, 2004. http://dx.doi.org/10.1002/asi.20009

W. Fan, P. Pathak, and M. Zhou. Genetic-based approaches in ranking function discovery and optimization in information retrieval - a framework. Decision Support Systems, 2009. http://dx.doi.org/10.1016/j.dss.2009.04.005

D. E. Golberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.

R. L. Haupt and S. E. Haupt. Practical Genetic Algorithms. John Wiley & Sons, New Jersey, United States, second edition edition, 2004.

J. Horng and C. Yeh. Applying genetic algorithms to query optimization in document retrieval. Information Processing & Management, 36:737-759, 2000. http://dx.doi.org/10.1016/S0306-4573(00)00008-X

S. K. Kinoshita, P. M. d. Azevedo-Marques, R. R. PereiraJr., J. A. H. Rodrigues, and R. M. Rangayyan. Contentbased retrieval of mammograms using visual features related to breast density patterns. Journal of Digital Imaging, 20(2): 172-190, June 2007. http://dx.doi.org/10.1007/s10278-007-9004-0

F. Korn, B. Pagel, and C. Faloutsos. On the 'dimensionality curse' and the 'self-similarity blessing'. IEEE Trans, on Knowledge and Data Engineering, 13(1):96-111, 2001. http://dx.doi.org/10.1109/69.908983

H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Enginnering, 17(4):491-502, April 2005. http://dx.doi.org/10.1109/TKDE.2005.66

M. X. Ribeiro, A. J. M. Traina, C. Traina-Jr, and P. M. Azevedo-Marques. An association rule-based method to support medical image diagnosis with efficiency. IEEE Transactions on Multimedia, 10(2):277-285, 2008. http://dx.doi.org/10.1109/TMM.2007.911837

U. S. Cancer Statistics Working Group. United states cancer statistics: 1999-2005 incidence and mortality webbased report, atlanta (ga): Department of health and human services, centers for disease control and prevention, and national cancer institute., 2009. Available in http://apps.need.cdc.gov/uscs/.

L. Tamine, C. C., and M. Boughanem. Multiple query evaluation based on an enhanced geneticnext term algorithm. Information Processing & Management, 39(2):215-231,2003. http://dx.doi.org/10.1016/S0306-4573(02)00048-1

R. S. Torres, A. X. Falcão, M. A. Gonçalves, J. P. Papa, Z. B., W. Fan, and E. A. Fox. A genetic programming framework for content-based image retrieval. Journal of the American Society for Information Science and Technology, 42(2):283-292, 2009. http://dx.doi.org/10.1016/j.patcog.2008.04.010

A. Tsymbal, P. Cunningham, M. Pechenizkiy, and S. Puuronen. Search strategies for ensemble feature selection in medical diagnostics. In Proceedings of the 16th IEEE Symposium on Computer-Based Medical Systems, pages 124129, June 2003. http://dx.doi.org/10.1109/CBMS.2003.1212777

A. Tsymbal, M. Pechenizkiy, and P. Cunningham. Sequential genetic search for ensemble feature selection. In Proceedings of the International Joint Conferences on Artificial Intelligence, pages 877-882, August 2005. http://dx.doi.org/10.1109/CBMS.2003.1212777

C-M. Wanga and Y.-F. Huang. Evolutionary-based feature selection approaches with new criteria for data mining:: A case study of credit approval data. Expert Systems with Applications, 36(3 - Part 2):5900-5908, 2009. http://dx.doi.org/10.1016/j.eswa.2008.07.026

H. Yan, J. Zheng, Y. Jiang, C. Peng, and S. Xiao. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. Applied Soft Computing, 8:1105-1111,2008. http://dx.doi.org/10.1016/j.asoc.2007.05.017

T. Zhao, J. Lu, Y. Zhang, and Q. Xiao. Feature selection based on genetic algorithm for cbir. In IEEE Congress on Image and Signal Processing, volume 2, pages 495-499, 2008. http://dx.doi.org/10.1109/CISP.2008.90


Full Text

internal file

Sonstige Links