
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Yan Li, Simon C.K. Shiu, Sankar K. Pal, "Combining Feature Reduction and Case Selection in Building CBR Classifiers," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 3, pp. 415429, March, 2006.  
BibTex  x  
@article{ 10.1109/TKDE.2006.40, author = {Yan Li and Simon C.K. Shiu and Sankar K. Pal}, title = {Combining Feature Reduction and Case Selection in Building CBR Classifiers}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {18}, number = {3}, issn = {10414347}, year = {2006}, pages = {415429}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.40}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Combining Feature Reduction and Case Selection in Building CBR Classifiers IS  3 SN  10414347 SP415 EP429 EPD  415429 A1  Yan Li, A1  Simon C.K. Shiu, A1  Sankar K. Pal, PY  2006 KW  Casebased reasoning KW  CBR classifier KW  case selection KW  feature reduction KW  kNN principle KW  rough sets. VL  18 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] J. Kolodner, CaseBased Reasoning. Morgan Kaufmann, 1993.
[2] S.K. Pal and S.C.K. Shiu, Foundations of Soft CaseBased Reasoning. John Wiley, 2004.
[3] C.C. Hsu and C.S. Ho, “Acquiring Patient Data by an Intelligent Interface Agent with MedicineRelated Common Sense Reasoning,” Expert Systems with Applications: An Int'l J., vol. 17, no. 4, pp. 257274, 1999.
[4] T.W. Liao, “An Investigation of a Hybrid CBR Method for Failure Mechanisms Identification,” Eng. Applications of Artificial Intelligence, vol. 17, no. 1, pp. 123134, 2004.
[5] E. Kalapanidas and N. Avouris, “ShortTerm Air Quality Prediction Using a CaseBased Classifier,” Environmental Modelling and Software, vol. 16, no. 3, pp. 263272, 2001.
[6] K.E. Emam, S. Benlarbi, N. Goel, and S.N. Rai, “Comparing CaseBased Reasoning Classifiers for Predicting High Risk Software Components,” J. Systems and Software, vol. 55, no. 3, pp. 301320, 2001.
[7] J.M. Garrell i Guiu, E. Golobardes i Ribé, E. Bernadó i Mansilla, and X. Llorà i Fàbrega, “Automatic Diagnosis with Genetic Algorithms and CaseBased Reasoning,” Artificial Intelligence in Eng., vol. 13, no. 4, pp. 367372, 1999.
[8] M.Q. Xu, K. Hirota, and H. Yoshino, “A Fuzzy Theoretical Approach to Representation and Inference of Case in CISG,” Int'l J. Artificial Intelligence and Law, vol. 7, nos. 23, pp. 259272, 1999.
[9] P.P. Bonissone and W. Cheetham, “Financial Application of Fuzzy CaseBased Reasoning to Residential Property Valuation,” Proc. Sixth IEEE Int'l Conf. Fuzzy Systems (FUZZIEEE97), pp. 3744, 1997.
[10] M.L. Masher and D.M. Zhang, “CADSYN: A CaseBased Design Process Model,” Artificial Intelligence for Eng. Design, Analysis and Manufacturing, vol. 7, no. 2, pp. 97110, 1993.
[11] T.R. Hinrihs, Problem Solving in Open Worlds. Hillsdate, N.J.: Lawrence Erlbaum Assoc., 1992.
[12] P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. Prentice Hall, 1982.
[13] I.T. Jolliffe, Principle Component Analysis. Springer, 1986.
[14] P. Geladi, H. Isaksson, L. Lindqvist, S. Wold, and K. Esbensen, “Principle Component Analysis of Multivariate Images,” Chemometrics and Intelligent Laboratory Systems, vol. 5, no. 3, pp. 209220, 1989.
[15] M.A. Kramer, “Nonlinear Principal Component Analysis Using Autoassociative Neural Networks,” AIChE J., vol. 37, no. 2, pp. 233243, Feb. 1991.
[16] P. Mitra, C.A. Murthy, and S.K. Pal, “Unsupervised Feature Selection Using Feature Similarity,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 301312, Mar. 2002.
[17] R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, “Diagnosis of Multiple Cancer Types By Shrunken Centroids Of Gene Expression,” Proc. Nat'l Academy of Sciences (PNAS), vol. 99, no. 10, pp. 65676572, 2002.
[18] Z. Pawlak, “Rough Sets,” Int'l J. Computer and Information Science, vol. 11, no. 5, pp. 341356, 1982.
[19] Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, 1991.
[20] F.E. H. Tay and L. Shen, “Fault Diagnosis Based on Rough Set Theory,” Eng. Applications of Artificial Intelligence, vol. 16, no. 1, pp. 3943, 2003.
[21] S.K. Pal and P. Mitra, “Multispectral Image Segmentation Using Rough Set Initialized EM Algorithm,” IEEE Trans. Geoscience and Remote Sensing, vol. 40, no. 11, pp. 24952501, 2002.
[22] C.C. Chan, “A Rough Set Approach to Attribute Generalization in Data Mining,” Information Science, vol. 107, nos. 14, pp. 169176, 1998.
[23] R. Jensen and Q. Shen, “FuzzyRough Attribute Reduction with Application to Web Categorization,” Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469485, 2004.
[24] L. Shen and H.T. Loh, “Applying Rough Sets to Market Timing Decisions,” Decision Support Systems, vol. 37, no. 4, pp. 583597, 2004.
[25] A. Skowron and C. Rauszer, “The Discernibility Matrices and Functions in Information Systems,” Intelligent Decision Support— Handbook of Applications and Advances of the Rough Sets Theory, R. Slowinski, ed., pp. 331362, 1992.
[26] Q. Shen and A. Chouchoulas, “A RoughFuzzy Approach for Generating Classification Rules,” Pattern Recognition, vol. 35, no. 11, pp. 341354, 2002.
[27] J. Han, X. Hu, and T.Y. Lin, “Feature Subset Selection Based on Relative Dependency Between Attributes,” Proc. Fourth Int'l Conf. Rough Sets and Current Trends in Computing (RSCTC '04), pp. 176185, 2004.
[28] H. Brighton and C. Mellish, “Advances in Instance Selection for InstanceBased Learning Algorithms,” Data Mining and Knowledge Discovery, vol. 6, no. 2, pp. 153172, 2002.
[29] P.E. Hart, “The Condensed Nearest Neighbor Rule,” Inst. of Electrical and Electronics Eng. Trans. Information Theory, vol. 14, pp. 515516, 1968.
[30] D.R. Wilson and L. Dennis, “Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,” IEEE Trans. Systems, Man, and Cybernetics, vol. 2, no. 3, pp. 408421, 1972.
[31] G.W. Gates, “The Reduced Nearest Neighbor Rule,” IEEE Trans. Information Theory, vol. 18, no. 3, pp. 431433, 1972.
[32] G.L. Ritter, H.B. Woodruff, S.R. Lowry, and T.L. Isenhour, “An Algorithm for the Selective Nearest Neighbor Decision Rule,” IEEE Trans. Information Theory, vol. 21, no. 6, pp. 665669, 1975.
[33] I. Tomek, “An Experiment with the Edited NearestNeighbor Rule,” IEEE Trans. Systems, Man, and Cybernetics, vol. 6, no. 6, pp. 448452, 1976.
[34] B. Smyth and E McKenna, “FootprintBased Retrieval,” Proc. Fourth Int'l Conf. CaseBased Reasoning, pp. 343357, 1999.
[35] B. Smyth and E. Mckenna, “Building Compact Competent Case Bases,” Proc. Third Int'l Conf. CaseBased Reasoning, pp. 329342, 1999.
[36] K. Racine and Q. Yang, “Maintaining Unstructured Case Bases,” Proc. Second Int'l Conf. CaseBased Reasoning, pp. 553564, 1997.
[37] G. Cao, S.C.K. Shiu, and X.Z. Wang, “A FuzzyRough Approach for the Maintenance of Distributed CaseBased Reasoning Systems,” Soft Computing, vol. 7, no. 8, pp. 491499, 2003.
[38] M.M. Astrahan, “Speech Analysis by Clustering, or the Hyperphoneme Method,” Stanford A.I. Project Memo, Stanford Univ., Calif., 1970.
[39] P. Mitra, C.A. Murthy, and S.K. Pal, “Density Based Multiscale Data Condensation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 734747, 2002.
[40] C.L. Chang, “Finding Prototypes for Nearest Neighbor Classifiers,” IEEE Trans. Computers, vol. 23, no. 11, pp. 11791184, Nov. 1974.
[41] P. Domingos, “Rule Induction and InstanceBased Learning: A Unified Approach,” Proc. 14th Int'l Joint Conf. Artificial Intelligence, pp. 12261232, 1995.
[42] S. Salzberg, “A Nearest Hyperrectangle Learning Method,” Machine Learning, vol. 6, no. 3, pp. 251276, 1991.
[43] S.K. Pal and P. Mitra, “Case Generation Using Rough Sets with Fuzzy Representation,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 3, pp. 292300, 2004.
[44] V.N. Vapnik, Statistical Learning Theory. Wiley, 1998.
[45] V.N. Vapnik, The Nature of Statistical Learning Theory. Springer, 1999.
[46] D. Kim and C. Kim, “Forecasting Time Series with Genetic Fuzzy Predictor Ensemble,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 523535, 1997.
[47] S.C.K. Shiu, D.S. Yeung, C.H. Sun, and X.Z. Wang, “Transforming Case Knowledge to Adaptation Knowledge: An Approach for CaseBase Maintenance,” Computational Intelligence, vol. 17, no. 2, pp. 295313, 2001.
[48] UCI, Learning Data Repository, http://www.ics.uci.edu/~mlearnMLRepository.html , 2005.
[49] D.D. Lewis, Reuters21578 Text Categorization Test Collection Distribution 1.0, http://www.research.att.com~lewis, 1999.
[50] S.R. Gunn, “Support Vector Machines for Classification and Regression,” technical report, Image Speech and Intelligent Systems Research Group, Univ. of Southampton, 1997.