International Journal of Intelligent Systems and Applications(IJISA)

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

Published By: MECS Press

IJISA Vol.5, No.12, Nov. 2013

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Full Text (PDF, 871KB), PP.34-52

Views:251   Downloads:7


Julie M. David, Kannan Balakrishnan

Index Terms

ANFIS, Data Mining, FIS, Learning Disability, Membership Function


Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Cite This Paper

Julie M. David, Kannan Balakrishnan,"Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.12, pp.34-52, 2013. DOI: 10.5815/ijisa.2013.12.03


[1]Julie M. David, Pramod K.V., “Paper on Prediction of Learning Disabilities in School Age children using Data Mining Techniques”, In: Proceedings of AICTE Sponsored National Conf. on Recent Developments and Applications of Probability Theory, Random Process and Random Variables in Computer Science, T. Thrivikram, P. Nagabhushan, M.S. Samuel (eds), 2008, pp139-146.

[2]Sally Jo Cunningham, Geoffrey Holmes, Developing innovative applications in agricultural using data mining-In the Proceedings of the Southeast Asia Regional Computer Confederation Conference, 1999

[3]Frawley and Piaatetsky, Shaping Knowledge Discovery in Database; an Overview, The AAAI/MIT press, Menlo Park, 1996.

[4]Hsinchun Chen, Sherrilynne S. Fuller, Carol Friedman and William Hersh. Knowledge Discovery in Data Mining and Text Mining in Medical Informatics, Chapter 1, 2005, pp3-34.

[5]Chapple Mike, Guide, http://databases.about.Com/od/datamining/g/classification.htm 

[6]Julie M. David, Kannan Balakrishnan, Significance of Classification Techniques in Prediction of Learning Disabilities in School Age Children, Int. J. of Artificial Intelligence & Applications, 1(4), DOI:10.5121/ijaia.2010.1409, Oct.2010, pp111-120.

[7]Blackwell Synergy, Learning Disabilities & Research Practice, Volume 22, 2007.

[8]Julie M. David, Kannan Balakrishnan, Prediction of Key Symptoms of Learning Disabilities in School-Age Children using Rough Sets, Int. J. of Computer and Electrical Engineering, 3(1), Feb. 2011, pp163-169

[9]Jiye Li, Nick Cercone: Assigning Missing Attribute Values Based on Rough Sets Theory, IEEE International Conference on Granular Computing, May 2006, pp607-610.

[10]Grzymala-Busse, J.W., Hu, M.: A Comparison of Serveral Approaches to Missing Attribute Values in Data Mining. Ziarko, W., Yao. Y., (Eds.): RSCTC 2000, LNAI 2005 (2001) pp378–385

[11]Fatma Khcherem, Abdelfettah Bouri: Fuzzy Logic and Investment Strategy Global Economy & Finanace Journal, 2(2), 2009, pp 22-37.

[12]Zadeh, L.A: The Concept of Linguistic Variable and its Application to Approximate Reasoning, Inf. Sciences, 8, 1975, pp199-249 

[13]Julie M. David, Kannan Balakrishnan, Attribute Reduction and Missing Value Imputing with ANN: Prediction of Learning Disabilities, Int. J. of Neural Computing, Springer-Verlag London Limited, DOI: 10.1007/s00521-011-0619-1, 21 (7), Oct.2012, pp 1757-1763.

[14]Kenneth A. Kavale: Identifying Specific Learning Disability - Is Responsiveness to Intervention the Answer?, J. of LDs, 38, 2005, pp553-562.

[15]Benjamin J. Lovett: Extended Time Testing Accommodations for Students with Disabilities: Answers to 5 Fundamental Questions, Review of Edu. Research, J. of LDs, 80, 2010, pp611-638.

[16]Noona Kiuru, et. al.: Students with Reading and Spelling Disabilities-Peer Groups and Educational Attainment in Secondary Education, Journal of Learning Disabilities, 44, 2011, pp556-569.

[17]Chen, S.M., Chen, H.H.: Estimating Null Values in the Distributed Relational Databases Environments, International Journal on Cybernetics and Systems, 31, 2000, pp851-871.

[18]Chen, S.M., Huang, C.M.: Generating Weighted Fuzzy Rules from Relational Database Systems for Estimating Null Values Using Genetic Algorithms. IEEE Transactions on Fuzzy Systems. 11, 2003, pp 495-506. 

[19]Quinlan, J.R.: C4.5 - Programs for Machine Learning. Morgan Kaufmann, San Mateo, USA, 1993.

[20]Friedman, J., et al.: Lazy Decision Trees. Proceedings of the 13th National Conference on Artificial Intelligence, 1996, pp717-724.

[21]Lakshminarayan, K., et al.: Imputation of Missing Data Using Machine Learning Techniques. KDD-1996, pp140-145.

[22]Magnani, M.: Techniques for Dealing with Missing Data in Knowledge Discovery Tasks. Available at: http: //magnanim. web.cs. data/pdf/missingdata.pdf, Version of June 2004.

[23]Kahl, F., et al.: Minimal Projective Reconstruction Including Missing Data. IEEE Trans. Pattern Anal. Mach. Intell., 23(4), 2001, pp418-424.

[24]Gessert, G.: Handling Missing Data by Using Stored Truth Values. SIGMOD Record, 20(3), 2001, pp30-42.

[25]Pesonen, E., et al.: Treatment of Missing Data Values in a Neural Network Based Decision Support System for Acute Abdominal Pain. Artificial Intelligence in Medicine, 13(3), 1998, pp139-146.

[26]Ramoni, M., Sebastiani, P.: Robust Learning with Missing Data. Machine Learning, 45(2), 2001, pp147-170.

[27]Pawlak, M., Kernel: Classification Rules from Missing Data. IEEE Transactions on Information Theory, 39(3), 1993, pp979-988

[28]Yao, Y.Y.: A Comparative Study of Fuzzy Sets and Rough Sets, Information Sciences, 109 (1-4), 1998, pp. 227-242

[29]Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Boston, 1991.

[30]Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic, Theory and Applications, Prentice Hall, New Jersey, 1995

[31]Chanas, S., Kuchta, D.: Further remarks on the relation between rough and fuzzy sets, Fuzzy Sets and Systems, 47, 1992, pp391-394.

[32]Zadeh, L.A.: Forward, in: Orlowska, E. (Ed.): Incomplete Information: Rough Set Analysis, Physica-Verlag, Heidelberg, 1998, pp. v-vi.

[33]Ossi Nykanen: Inducing Fuzzy Model for Students Classification, Educational Technology & Society, 9 (2), 2006, pp223-234.

[34]Rubens, N.O.,: The Application of Fuzzy Logic to the Construction of the Ranking Function of Information Retrieval System, Computer Modelling and New Technologies, 10 (1), 2006, pp20-27.

[35]Bordogna, G, Pasi, G.: Handling vagueness in information retrieval systems, Proceedings of the Second New Zealand Int. Two-Stream Conf. on Artificial Neural Networks and Expert Systems, 1995, pp110-114.

[36]Miyamoto, S.:Fuzzy sets in Information Retrieval and Cluster Analysis, Kluver Academic Publishers, 1990.

[37]Ogawa, Y., Morita, T., Kobayashi, K.: A Fuzzy Document Retrieval System Using the Key Word Connection Matrix and A Learning Method, Fuzzy Sets and Systems 39, 1991, pp163-179.

[38]Baeza Yates, R., Ribeiro Neto, B.: Modern Information Retrieval, ACM Press, 1999.

[39]Tung-Kuang Wu, Shian Chang Huang,Ying Ru: Evaluation of ANN and SVM Classifiers as Predictors to the Diagnosis of Students with LDs, J. of Expert Systems with Applications, 34 (3), 2008, pp1846-1856.

[40]Maitrei Kohli, Prasad, T.V.: Identifying Dyslexic Students by Using Artificial Neural Networks, Proc. of the World Congress on Engg. I, 2010, 

[41]Rod Paige (Secretary), US Department of Education, Twenty-fourth Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act-To Assure the Free Appropriate Public Education of all Children with Disabilities, 2002.

[42]Julie M. David, Kannan Balakrishnan, Machine Learning Approach for Prediction of Learning Disabilities in School Age Children, Int. J. of Computer Applications, ISSN-0975-8887, 9(10), Nov. 2010, pp7-14.

[43]Julie M. David, Kannan Balakrishnan, Prediction of Learning Disabilities in School-Age Children using SVM and Decision Tree, Int.J. of Computer Science and Information Technology, ISSN 0975-9646, 2(2), Mar-Apr. 2011, pp829-835.

[44]Julie M. David, Kannan Balakrishnan, “Paper on Prediction of Learning Disabilities in School Age Children using Decision Tree”. In: Proceedings of the Int. Conf. on Recent Trends in Network Communications- CCIS, 90(3), N. Meghanathan, Selma Boumerdassi, Nabendu Chaki, Dhinaharan Nagamalai (eds), Springer-Verlag Berlin Heidelberg, ISSN:1865-0929(print) 1865-0937(online), ISBN 978-3-642-14492-9(print) 978-3-642-14493-6(online), DOI : 10.1007/978-3-642-14493-6_55, 2010, pp533-542.

[45]Crealock Carol, Kronick Doreen. Children and Young People with Specific Learning Disabilities, Guides for Special Education, No. 9, UNESCO, 1993.

[46]Julie M. David, Kannan Balakrishnan, “Paper on Prediction of Frequent Signs of Learning Disabilities in School Age Children using Association Rules”. In: Proceedings of the Int. Conf. on Advanced Computing, ICAC 2009, MacMillion Publishers India Ltd., NYC, ISBN 10:0230-63915-1, ISBN 13:978-0230-63915-7, 2009, pp202–207.

[47]Zhang, S.C., et al.: Information Enhancement for Data Mining. IEEE Intelligent Systems, 19(2), 2004, pp12-13.

[48]Zhang, S.C., et al.: Missing is useful - Missing Values in Cost-Sensitive Decision Trees, IEEE Transactions on Knowledge and Data Engineering, 17(12), 2005, pp1689-1693.

[49]Qin, Y.S., et al.: Semi-Parametric Optimization for Missing Data Imputation. Applied Intelligence, 27(1), 2007, pp79-88.

[50]Zhang, C.Q., et al.: An Imputation Method for Missing Values, PAKDD, LNAI, 4426, 2007, pp1080–1087.

[51]Han Jiawei, Kamber Micheline, Pei Jian: Data Mining-Concepts and Techniques, Third Edition, Morgan Kaufmann - Elsevier Publishers, ISBN : 978-93-80931-91-3, 2011.

[52]Zhang, S.C., et al.: Optimized Parameters for Missing Data Imputation, PRICAI06, 2006, pp1010-1016.

[53]Wang, Q., Rao, J.,: Empirical Likelihood-based Inference in Linear Models with Missing Data, Scand. Journal of Statist., 29, 2002, 563-576.

[54]Wang, Q., Rao, J. N. K.: Empirical likelihood-based inference under imputation for missing response data, Ann. Statist., 30, 2002, pp896-924.

[55]Chen, J., Shao, J., Jackknife: Variance Estimation For Nearest-Neighbor Imputation, Journal of Amer. Statist. Assoc., 96, 2001, pp260-269.

[56]Lall, U., Sharma, A.: A Nearest-Neighbor Bootstrap For Resampling Hydrologic Time Series, Water Resource, Res. 2001, 32, 1996, pp679–693.

[57]Tan Pang-Ning, Steinbach Michael, Kumar Vipin : Introduction to Data Mining, Low Price Edition, Pearson Education, Inc., ISBN 978-81-317-1472-0, 2008.

[58]Julie M. David, Kannan Balakrishnan: A New Decision Tree Algorithm for Prediction of Learning Disabilities, Journal of Engineering Science and Technology, School of Engineering, Taylor's University, Malaysia. 8 (2), April 2013, pp120-132.

[59]Zadeh, L.A.,: Fuzzy sets, Information and control, 8, 1965, pp 338-353.

[60]Urathal U alias Swathiga Sri, Chandrasekar, C.: An Efficient Fuzzy based Congestion Control Technique for Wireless Sensor Networks, International Journal of Computer Applications, 40(14), Feb 2012, pp47-55.

[61]Maged Marghany, Mazlan Hashim, Farideh Moradi: Object recognitions in RADARSAT-1 SAR Data using Fuzzy Classification, International Journal of the Physical Sciences, Academic Journals, 6(16), 2011, pp3933-3938.

[62]Zadeh, L.A.: Fuzzy sets. Infor. Control, 8, 1965, pp338-353.

[63]Ross, T.J.: Fuzzy Logic with Engineering Applications, Second Edition, John Wiley & Sons Ltd., 2004.

[64]Fuzzy Logic Toolbox User’s Guide , The MathWorks Inc., 2004.

[65]Zadeh, L.A: The Concept of Linguistic Variable and its Application to Approximate Reasoning, Inf. Sciences, 8, 1975, pp199-249. 

[66]Zadeh, L.A: The Concept of Linguistic Variable and its Application to Approximate Reasoning, Inf. Sciences, 9, 1975, pp43-80.

[67]Rahib Abiyeb, Vasif, H., et. al.:Elecricity Consumtion Prediction Model using Neuro-Fuzzy System, World Academy of Science, Engineering and Technology, 8, 2005.

[68]Ajith Abraham: Neuro-Fuzzy Systems: State-of-the-Art Modeling Techniques, Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Lecture Notes in Computer Science, 2084, Springer-Verlag Germany, Jose Mira, Alberto Prieto (Eds.), ISBN 3540422358, Spain, 2001, pp269-276. 

[69]Shantakumar B. Patil, Kumaraswamy, Y.S.: Intelligent and Effective Heart Attack Prediction System using Data Mining and ANN, Europian Journal of Scientific Research, 31(4), ISSN 1450-216X, 2009, pp642-656.

[70]Corona-Nakamura, M. A., Ruelas, R., Ojeda Magana, B., Andina, D.: Classification of Domestic Water Consumption Using an ANFIS Model, World Automation Congress, Waikoloa, Hawaii, EEUU, IEEE Computer Society, 2008.

[71]Sivarao, et. al.: GUI Based Mamdani Fuzzy Inference System Modeling to Predict Surface Roughness in Laser Machining, International Journal of Electrical & Computer Sciences, 9(9), 2009, pp281-288.

[72]Sidda Reddy, B., Suresh Kumar, J., Vijaya Kumar Reddy, K.: Prediction of Roughness in Turning using Adaptive Neuro-Fuzzy Inference System, Jordan Journal of Mechanical and Industrial Engineering, 3(4), Dec. 2009, pp252-258.

[73]Erdem Buyukbingol, Arzu Sisman, Murat Akyildiz, Ferda Nur Alparslan, Adeboye Adeja: Adaptive neuro-fuzzy inference system (ANFIS): A New Approach to Predictive Modeling in QSAR Applications: A Study of Neuro-Fuzzy modeling of PCP-based NMDA Receptor Antagonists, Bioorganic & Medicinal Chemistry, 15(12), 2007, pp4265-4282.

[74]Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro –Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence, PHI Learning Pvt. Ltd, New Delhi, 2008