International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.8, No.1, Jan. 2016

Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis

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D. Asir Antony Gnana Singh, E. Jebamalar Leavline, R. Priyanka, P. Padma Priya

Index Terms

Attribute reduction;Naive Bayes classifier;genetic algorithm


The technological growth generates the massive data in all the fields. Classifying these high-dimensional data is a challenging task among the researchers. The high-dimensionality is reduced by a technique is known as attribute reduction or feature selection. This paper proposes a genetic algorithm (GA)-based features selection to improve the accuracy of medical data classification. The main purpose of the proposed method is to select the significant feature subset which gives the higher classification accuracy with the different classifiers. The proposed genetic algorithm-based feature selection removes the irrelevant features and selects the relevant features from original dataset in order to improve the performance of the classifiers in terms of time to build the model, reduced dimension and increased accuracy. The proposed method is implemented using MATLAB and tested using the medical dataset with various classifiers namely Naïve Bayes, J48, and k-NN and it is evident that the proposed method outperforms other methods compared.

Cite This Paper

D. Asir Antony Gnana Singh, E. Jebamalar Leavline, R. Priyanka, P. Padma Priya,"Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.1, pp.67-73, 2016. DOI: 10.5815/ijisa.2016.01.08


[1]Rokach Lior, "Genetic algorithm-based feature set partitioning for classification problems," Pattern Recogn, vol. 41, pp.1676-1700, 2008.  

[2]Magnus Erik and Hvass Pedersen, Genetic Algorithms for Feature Selection in Data Mining, Pedersen (971055) Daimi, University of Aarhus, November 2003.

[3]Analoui, M., and M. Fadavi Amiri. "Feature reduction of nearest neighbor classifiers using genetic algorithm." World Acad Sci Eng Technol, vol. 17, pp. 36-39, 2003. 

[4]Lanzi, Pier Luca, "Fast feature selection with genetic algorithms: a filter approach," IEEE International Conference on Evolutionary Computation, pp. 537-540, 1997.

[5]Zhang, Zili, and Pengyi Yang, "An ensemble of classifiers with genetic algorithm Based Feature Selection," IEEE intelligent informatics bulletin, vol. 9, pp. 18-24, 2008.

[6]Zhuo, Li, Jing Zheng, Xia Li, Fang Wang, Bin Ai, and Junping Qian. "A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine," Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, pp. 71471J-71471J, 2008. 

[7]Aziz, Amira Sayed A., Ahmad Taher Azar, Mostafa A. Salama, Aboul Ella Hassanien, and SE-O. Hanafy, "Genetic algorithm with different feature selection techniques for anomaly detectors generation." IEEE Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 769-774, 2013. 

[8]Jourdan, Laetitia, Clarisse Dhaenens, and El-Ghazali Talbi, "A genetic algorithm for feature selection in data-mining for genetics." 4th Metaheuristics International Conference Porto, pp. 29-34, 2001.  

[9]Chtioui, Younes, Dominique Bertrand, and Dominique Barba, "Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision." J Sci Food Agric, vol. 76, pp. 77-86, 1998.

[10]Huang, Cheng-Lung, and Chieh-Jen Wang, "A GA-based feature selection and parameters optimization for support vector machines", Expert Syst Appl, vol. 31, pp. 231-240, 2006.

[11]Sun, Yi, and Lijun Yin. "A genetic algorithm based feature selection approach for 3D face recognition." The Biometric Consortium Conference, (Hyatt Regency Crystal City, Arlington, Virginia USA), 2005.

[12]Vafaie, Haleh, and Ibrahim F. Imam, "Feature selection methods: genetic algorithms vs. reedy-like search," International Conference on Fuzzy and Intelligent Control Systems, 1994.

[13]Srikrishna, A., B. Eswara Reddy, and V. Sesha Srinivas. "Automatic Feature Subset Selection using Genetic Algorithm for Clustering." Int J Recent Trends Eng Tech, vol. 9, pp. 85-89, 2013.

[14]Vafaie, Haleh, and Kenneth De Jong. "Genetic algorithms as a tool for feature selection in machine learning." Fourth IEEE International Conference on Tools with Artificial Intelligence, Arlington, VA, pp. 200 – 203, 1992.

[15]Maulik, Ujjwal, and Sanghamitra Bandyopadhyay. "Genetic algorithm-based clustering technique." Pattern Recogn, vol. 33, pp. 1455-1465, 2000.

[16]Siedlecki, Wojciech, and Jack Sklansky. "A note on genetic algorithms for large-scale feature selection." Pattern Recogn Lett, vol. 10, pp. 335-347, 1989.

[17]Sarawat Anam, Md. Shohidul Islam, M.A. Kashem, M.N. Islam, M.R. Islam and M.S. Islaml. "Face recognition using genetic algorithm and back propagation neural network." International MultiConference of Engineers and Computer Scientists. 2009.

[18]Cho, Sung-Bae. "Pattern recognition with neural networks combined by genetic algorithm," Fuzzy Set Syst, vol. 103, pp. 339-347, 1999. 

[19]Ziomek, W., M. Reformat and E. Kuffel. "Application of genetic algorithms to pattern recognition of defects in GIS," IEEE T Dielect El In, vol. 7, pp. 161-168, 2000.

[20]Chun, Dae N. and Hyun S. Yang. "Robust image segmentation using genetic algorithm with a fuzzy measure," Pattern Recogn, vol. 29, pp. 1195-1211, 1996. 

[21]Reeves, CR. "A genetic algorithm for flowshop sequencing." Comput Oper Res, vol. 22, pp. 5-13, 1995.

[22]Hunter, A. “Feature selection using probabilistic neural networks,” Neural Comput Appl, vol. 9, pp. 124-132, 2000. 

[23]Rubiyah Yusof, Uswah Khairuddin and Marzuki Khalid. “A New Mutation Operation for Faster Convergence in Genetic Algorithm Feature Selection,” IJICIC, vol. 8, pp. 7363-7378, 2012. 

[24]Martinez-Arroyo, M. and Sucar, LE. “Learning an optimal naive bayes classifier”, 18th IEEE International Conference on Pattern Recognition, vol. 3, pp. 1236-1239, 2006.

[25]Zia, T., Abbas, Q., and Akhtar, MP. “Evaluation of Feature Selection Approaches for Urdu Text Categorization,” I.J. Intelligent Systems and Applications, vol. 7, pp. 33-40, 2015.

[26]Kotsiantis, SB. “Supervised Machine Learning: A Review of Classification Techniques,” Informatica, vol. 31, pp. 249-268, 2007.

[27]Cufoglu, A., Lohi, M., and Madani, K. “Classification accuracy performance of Naive Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1)-comparative study,”  IEEE International Conference on Computer Engineering & Systems, pp. 210-215, 2008. 

[28]Jaber Karimpour,Ali A. Noroozi and Adeleh Abadi. “The Impact of Feature Selection on Web Spam Detection,” I.J. Intelligent Systems and Applications, vol. 4, pp. 61-67, 2012.