Mining competent case bases for case-based reasoning

Case-based reasoning relies heavily on the availability of a highly competent case base to make high-quality decisions. However, good case bases are difficult to come by. In this paper, we present a novel algorithm for automatically mining a high-quality case base from a raw case set that can preserve and sometimes even improve the competence of case-based reasoning. In this paper, we analyze two major problems in previous case-mining algorithms. The first problem is caused by noisy cases such that the nearest neighbor cases of a problem may not provide correct solutions. The second problem is caused by uneven case distribution, such that similar problems may have dissimilar solutions. To solve these problems, we develop a theoretical framework for the error bound in case-based reasoning, and propose a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently. We support our theory and algorithm with extensive empirical evaluation using different benchmark data sets.

[1]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[2]  Qiang Yang,et al.  Case Retrieval Using Nonlinear Feature-Space Transformation , 2004, ECCBR.

[3]  Chris Mellish,et al.  On the Consistency of Information Filters for Lazy Learning Algorithms , 1999, PKDD.

[4]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[5]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[6]  Carla E. Brodley,et al.  Automatic Algorith/Model Class Selection , 1993, International Conference on Machine Learning.

[7]  Bart Kosko,et al.  Neural networks for signal processing , 1992 .

[8]  Gavin C. Cawley,et al.  Optimally regularised kernel Fisher discriminant analysis , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[9]  Barry Smyth,et al.  Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems , 1995, IJCAI.

[10]  Sebastian Mika,et al.  Kernel Fisher Discriminants , 2003 .

[11]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[12]  Tony R. Martinez,et al.  Instance Pruning Techniques , 1997, ICML.

[13]  Padraig Cunningham,et al.  An Analysis of Case-Base Editing in a Spam Filtering System , 2004, ECCBR.

[14]  Juan M. Corchado,et al.  Automating the construction of CBR systems using kernel methods , 2001, Int. J. Intell. Syst..

[15]  R. Tibshirani,et al.  Penalized Discriminant Analysis , 1995 .

[16]  Tony R. Martinez,et al.  Reduction Techniques for Instance-Based Learning Algorithms , 2000, Machine Learning.

[17]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[18]  Barry Smyth,et al.  Competence-Guided Case-Base Editing Techniques , 2000, EWCBR.

[19]  Steven Salzberg,et al.  A Nearest Hyperrectangle Learning Method , 1991, Machine Learning.

[20]  Barry Smyth,et al.  Modelling the Competence of Case-Bases , 1998, EWCBR.

[21]  G. Gates,et al.  The reduced nearest neighbor rule (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[22]  Qiang Yang,et al.  A Case‐Addition Policy for Case‐Base Maintenance , 2001 .

[23]  Mykola Galushka,et al.  Towards Dynamic Maintenance of Retrieval Knowledge in CBR , 2002, FLAIRS.

[24]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

[25]  Agnar Aamodt,et al.  Combining Case Based Reasoning and Data Mining - A way of revealing and reusing RAMS experience , 1998 .

[26]  Qiang Yang,et al.  Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance , 1999, IJCAI.

[27]  David G. Stork,et al.  Pattern Classification , 1973 .

[28]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[29]  Rm Cameron-Jones,et al.  Instance Selection by Encoding Length Heuristic with Random Mutation Hill Climbing , 1995 .

[30]  R. Tibshirani,et al.  Flexible Discriminant Analysis by Optimal Scoring , 1994 .

[31]  G. Gates.  The Reduced Nearest Neighbor Rule , 1998 .

[32]  I. Tomek.  An Experiment with the Edited Nearest-Neighbor Rule , 1976 .

[33]  Jianping Zhang,et al.  Selecting Typical Instances in Instance-Based Learning , 1992, ML.

[34]  David McSherry,et al.  Automating case selection in the construction of a case library , 2000, Knowl. Based Syst..

[35]  David W. Aha,et al.  Learning Representative Exemplars of Concepts: An Initial Case Study , 1987 .

[36]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[37]  Chin-Liang Chang,et al.  Finding Prototypes For Nearest Neighbor Classifiers , 1974, IEEE Transactions on Computers.

[38]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[39]  Barry Smyth,et al.  Competence-guided Editing Methods for Lazy Learning , 2000, ECAI.

[40]  Carla E. Brodley,et al.  Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection , 1993 .

[41]  Volker Roth,et al.  Nonlinear Discriminant Analysis Using Kernel Functions , 1999, NIPS.

[42]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[43]  Qiang Yang,et al.  Competence Driven Case-Base Mining , 2005, AAAI.

[44]  Hugh B. Woodruff,et al.  An algorithm for a selective nearest neighbor decision rule (Corresp.) , 1975, IEEE Trans. Inf. Theory.

[45]  Padraig Cunningham,et al.  Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control , 1997, ICCBR.

[46]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[47]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[48]  Pedro M. Domingos.  Rule Induction and Instance-Based Learning: A Unified Approach , 1995, IJCAI.

[49]  Padraig Cunningham,et al.  ECUE: A Spam Filter that Uses Machine Leaming to Track Concept Drift , 2006, ECAI.

[50]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[51]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[52]  Barry Smyth,et al.  Competence Models and the Maintenance Problem , 2001, Comput. Intell..

[53]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[54]  Jieping Ye,et al.  Efficient Kernel Discriminant Analysis via QR Decomposition , 2004, NIPS.

[55]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[56]  Barry Smyth,et al.  Building Compact Competent Case-Bases , 1999, ICCBR.

[57]  Ian Witten,et al.  Data Mining , 2000 .