Abstract
A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, as an alternative to Sarkar’s fuzzy-rough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the experimental analysis, we evaluate our approach with both classical fuzzy-rough approximations (based on an implicator and a t-norm), as well as with the recently introduced vaguely quantified rough sets. Preliminary results are very good, and in general FRNN outperforms FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aha, D.: Instance-based learning algorithm. Machine Learning 6, 37–66 (1991)
Bhatt, R.B., Gopal, M.: FRID: Fuzzy-Rough Interactive Dichotomizers. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004), pp. 1337–1342 (2004)
Bian, H., Mazlack, L.: Fuzzy-Rough Nearest-Neighbor Classification Approach. In: Proceeding of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 500–505 (2003)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine (1998), http://archive.ics.uci.edu/ml/
Cohen, W.W.: Fast effective rule induction. In: Machine Learning: Proceedings of the 12th International Conference, pp. 115–123 (1995)
Cornelis, C., De Cock, M., Radzikowska, A.M.: Vaguely Quantified Rough Sets. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 87–94. Springer, Heidelberg (2007)
Cornelis, C., De Cock, M., Radzikowska, A.M.: Fuzzy Rough Sets: from Theory into Practice. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing. Wiley, Chichester (2008)
Cornelis, C., Hurtado Martín, G., Jensen, R., Slezak, D.: Feature Selection with fuzzy decision reducts. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 284–291. Springer, Heidelberg (2008)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Edwards, A.L.: An Introduction to Linear Regression and Correlation. W.H. Freeman, San Francisco (1976)
European Network for Fuzzy Logic and Uncertainty Modelling in Information Technology (ERUDIT), Protecting rivers and streams by monitoring chemical concentrations and algae communities, Computational Intelligence and Learning (CoIL) Competition (1999)
Greco, S., Inuiguchi, M., Slowinski, R.: Fuzzy rough sets and multiple-premise gradual decision rules. International Journal of Approximate Reasoning 41, 179–211 (2005)
Hong, T.P., Liou, Y.L., Wang, S.L.: Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets. In: Proceedings of the Joint Conference on Information Sciences, Advances in Intelligent Systems Research(2006)
Hsieh, N.-C.: Rule Extraction with Rough-Fuzzy Hybridization Method. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 890–895. Springer, Heidelberg (2008)
Jensen, R., Shen, Q.: Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems 15(1), 73–89 (2007)
Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley-IEEE Press (2008)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy K-nearest neighbor algorithm. IEEE Trans. Systems Man Cybernet. 15(4), 580–585 (1985)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. The Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Radzikowska, A.M., Kerre, E.E.: A comparative study of fuzzy rough sets. Fuzzy Sets and Systems 126(2), 137–155 (2002)
Sarkar, M.: Fuzzy-Rough nearest neighbors algorithm. Fuzzy Sets and Systems 158, 2123–2152 (2007)
Shen, Q., Chouchoulas, A.: A rough-fuzzy approach for generating classification rules. Pattern Recognition 35(11), 2425–2438 (2002)
Smola, A.J., Schölkopf, B.: A Tutorial on Support Vector Regression, NeuroCOLT2 Technical Report Series - NC2-TR-1998-030 (1998)
Wang, Y.: A new approach to fitting linear models in high dimensional spaces, PhD Thesis, Department of Computer Science, University of Waikato (2000)
Wang, X., Yang, J., Teng, X., Peng, N.: Fuzzy-Rough Set Based Nearest Neighbor Clustering Classification Algorithm. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3613, pp. 370–373. Springer, Heidelberg (2005)
Wang, X., Tsang, E.C.C., Zhao, S., Chen, D., Yeung, D.S.: Learning fuzzy rules from fuzzy samples based on rough set technique. Information Sciences 177(20), 4493–4514 (2007)
Witten, I.H., Frank, E.: Generating Accurate Rule Sets Without Global Optimization. In: Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco (1998)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jensen, R., Cornelis, C. (2011). Fuzzy-Rough Nearest Neighbour Classification. In: Peters, J.F., Skowron, A., Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Transactions on Rough Sets XIII. Lecture Notes in Computer Science, vol 6499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18302-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-18302-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18301-0
Online ISBN: 978-3-642-18302-7
eBook Packages: Computer ScienceComputer Science (R0)