Abstract
This paper describes proposed modifications to the K-NN classifier, i.e., Modified Fuzzy KNN (MFKNN) to address some complexity drawbacks of KNN. MFKNN calculates group prototypes from several patterns belonging to the same class and uses these prototypes for the recognition of patterns. Number of prototypes created by MFKNN classifier is dependent on the distance factor d. More prototypes are created for smaller value of d and vice versa.
Also a fuzzy logic layer is added to it to increase the prediction accuracy of the classifier. We have compared performance of original KNN and MFKNN using skin segmentation dataset. From the experimentation, one can conclude that performance of MFKNN is better than original KNN, in terms of percentage recognition rate and recall time per pattern, classification and classification time. MFKNN thus has increased the scope of original KNN for its application to large data sets, which was not possible previously.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
E. Rich, K. Knight, Artificial Intelligence, 2nd edn. (Tata McGraw-Hill, New Delhi, 1991)
R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance (Springer, Berlin, 2006)
E. Fix, J.L. Hodges, Discriminatory analysis, nonparametric discrimination: consistency properties. Technical Report 4 (USAF School of Aviation Medicine, Randolph Field, San Antonio, TX, 1951)
T.M. Cover, P.E. Hart, Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley, New York, 2001)
T.M. Cover, P.E. Hart, Nearest neighbor pattern classification. IEEE Trans. Inform. Theory IT-13(1), 21–27 (1967)
T. Mitchell, Machine Learning (McGraw-Hill, New York, 1997)
M.E. Hellman, The nearest neighbor classification rule with a reject option. IEEE Trans. Syst. Man Cybern. 3, 179–185 (1970)
K. Fukunaga, L. Hostetler, k-Nearest-neighbor Bayes-risk estimation. IEEE Trans. Inform. Theory 21(3), 285–293 (1975)
S.A. Dudani, The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6, 325–327 (1976)
T. Bailey, A. Jain, A note on distance-weighted k-nearest neighbor rules. IEEE Trans. Syst. Man Cybern. 8, 311–313 (1978)
S. Bermejo, J. Cabestany, Adaptive soft k-nearest-neighbour classifiers. Pattern Recogn. 33, 1999–2005 (2000)
A. Jozwik, A learning scheme for a fuzzy K-NN rule. Pattern Recogn. Lett. 1, 287–289 (1983)
J.M. Keller, M.R. Gray, J.A. Givens, A fuzzy K-NN neighbor algorithm. IEEE Trans. Syst. Man Cybern. SMC-15(4), 580–585 (1985)
P.S. Dhabe, S.G. Lade, S. Pingale, R. Prakash, M.L. Dhore, Modified K- nearest neighbor classifier using group prototypes and its application to fault diagnosis. CIIT Int. J Data Min. Knowl. Eng. (Impact Factor 0.621) 2(5) (2010), May, available online http://ciitresearch.org/dmkmmay2010.html
ITQON, K. Shunichi, I. Satoru, Improving performance of k-nearest neighbor classifier by test features. Springer Transactions of the Institute of Electronics, Information and Communication Engineers, 2001
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dhabe, P., Chugwani, M.P., Kahalekar, V.B. (2020). Modified K-Nearest Neighbor Fuzzy Classifier Using Group Prototypes and Its Application to Skin Segmentation. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-19562-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19561-8
Online ISBN: 978-3-030-19562-5
eBook Packages: EngineeringEngineering (R0)