Case-Based Reasoning and the Statistical Challenges II

Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims to develop computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. We will review in this paper the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are: similarity, memory organization, CBR learning, and case-base maintenance. The incremental aspect arising with the CBR paradigm will be considered as well as the life-time aspect of a CBR system. We will point out open problems within CBR that need to be solved. Finally we show on application how the CBR paradigm can be applied. The applications we are focusing on are meta-learning for parameter selection in technical systems, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR.

[1]  Peter Grünwald,et al.  Invited review of the book Statistical and Inductive Inference by Minimum Message Length , 2006 .

[2]  Petra Perner,et al.  Case-Based Reasoning and the Statistical Challenges , 2008, ECCBR.

[3]  Petra Perner,et al.  Introduction to Case-Based Reasoning for Signals and Images , 2008, Case-Based Reasoning on Images and Signals.

[4]  Carlo Zaniolo,et al.  An Adaptive Nearest Neighbor Classification Algorithm for Data Streams , 2005, PKDD.

[5]  Gerhard Wunsch.  Systemtheorie der Informationstechnik : eine Einführung in die Grundlagen , 1971 .

[6]  Dympna O'Sullivan,et al.  Medical Imagery in Case-Based Reasoning , 2008, Case-Based Reasoning on Images and Signals.

[7]  Petra Perner,et al.  Concepts for novelty detection and handling based on a case-based reasoning process scheme , 2007, Eng. Appl. Artif. Intell..

[8]  Petra Perner,et al.  CONCEPTUAL CLUSTERING AND CASE GENERALIZATION OF TWO‐DIMENSIONAL FORMS , 2006, Comput. Intell..

[9]  Kalyan Moy Gupta,et al.  Case-Based Collective Inference for Maritime Object Classification , 2009, ICCBR.

[10]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[11]  Christoph F. Eick,et al.  Distance Function Learning for Supervised Similarity Assessment , 2008, Case-Based Reasoning on Images and Signals.

[12]  Petra Perner.  Case-Based Reasoning on Images and Signals , 2008, Studies in Computational Intelligence.

[13]  Mobyen Uddin Ahmed,et al.  An Overview of Three Medical Applications Using Hybrid Case-Based Reasoning , 2012, Industrial Conference on Data Mining - Workshops.

[14]  Torgeir Dingsøyr.  A Lifecycle Process for Experience Databases , 1999, ICCBR Workshops.

[15]  Alexandre Hanft,et al.  Cases with a Life-Cycle , 1999, ICCBR Workshops.

[16]  Frans Coenen,et al.  Formalising optimal feature weight setting in case based diagnosis as linear programming problems , 2002, Knowl. Based Syst..

[17]  Petra Perner,et al.  Why Case-Based Reasoning Is Attractive for Image Interpretation , 2001, ICCBR.

[18]  Luigi Portinale,et al.  Retrieval in a Prototype-Based Case Library: A Case Study in Diabetes Therapy Revision , 1998, EWCBR.

[19]  Petra Perner,et al.  Case-base maintenance by conceptual clustering of graphs , 2006, Eng. Appl. Artif. Intell..

[20]  P. Hofstaetter.  [Similarity]. , 2020, Psyche.

[21]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[22]  Claus Weihs,et al.  Classification in music research , 2007, Adv. Data Anal. Classif..

[23]  Padraig Cunningham,et al.  A Taxonomy of Similarity Mechanisms for Case-Based Reasoning , 2009, IEEE Transactions on Knowledge and Data Engineering.

[24]  Erkki Oja,et al.  Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters , 2001, International Journal on Document Analysis and Recognition.

[25]  Peter Funk,et al.  A Case-Based Classification of Respiratory Sinus Arrhythmia , 2004, ECCBR.

[26]  Bir Bhanu,et al.  Concepts learning with fuzzy clustering and relevance feedback , 2002 .

[27]  Linda B. Smith.  From global similarities to kinds of similarities: the construction of dimensions in development , 1989 .

[28]  Michael Grüninger,et al.  Introduction , 2002, CACM.

[29]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[30]  Philip S. Yu,et al.  Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings , 2002 .

[31]  Sanja Petrovic,et al.  A Case-Based Reasoning Approach to Dose Planning in Radiotherapy , 2007 .

[32]  Petra Perner.  An architecture for a CBR image segmentation system , 1999 .

[33]  Edward E. Smith,et al.  Categories and concepts , 1984 .

[34]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[35]  Magdalena Topczewska,et al.  Improving the K-NN Classification with the Euclidean Distance Through Linear Data Transformations , 2004, ICDM.

[36]  Stefan Wess,et al.  Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning , 1993, EWCBR.

[37]  R. Duin,et al.  The dissimilarity representation for pattern recognition , a tutorial , 2009 .

[38]  Petra Perner,et al.  Prototype-based classification , 2008, Applied Intelligence.

[39]  Armin Stahl,et al.  Learning Feature Weights from Case Order Feedback , 2001, ICCBR.

[40]  Shane S. Sturrock,et al.  Time Warps, String Edits, and Macromolecules – The Theory and Practice of Sequence Comparison . David Sankoff and Joseph Kruskal. ISBN 1-57586-217-4. Price £13.95 (US$22·95). , 2000 .

[41]  David W. Aha,et al.  A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[42]  Alexandre Hanft,et al.  The Life Cycle of Test Cases in a CBR System , 2000, EWCBR.

[43]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[44]  M. Richter,et al.  Utility-Oriented Matching: A New Research Direction for Case-Based Reasoning , 2001 .

[45]  Roger C. Schank,et al.  Dynamic memory - a theory of reminding and learning in computers and people , 1983 .

[46]  Peter Funk,et al.  Building similarity metrics reflecting utility in case-based reasoning , 2006, J. Intell. Fuzzy Syst..

[47]  Gabriella Sanniti di Baja,et al.  Case-Based Reasoning for Image Segmentation by Watershed Transformation , 2008, Case-Based Reasoning on Images and Signals.

[48]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[49]  Petra Perner.  Incremental Normalization for CBR , 2012, Tran. CBR.

[50]  Rainer Schmidt,et al.  Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications , 2001, MLDM.

[51]  Ovidio Salvetti,et al.  Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications , 2008, ECCBR.

[52]  Carlos Soares,et al.  A Meta-Learning Method to Select the Kernel Width in Support Vector Regression , 2004, Machine Learning.

[53]  Osman M. Hegazy,et al.  Similarity Assessment Mechanism for Spatiotemporal Data Sets in Case-Based Reasoning , 2012, Industrial Conference on Data Mining - Workshops.

[54]  Michael M. Richter,et al.  Image processing in case-based reasoning , 2005, Knowl. Eng. Rev..

[55]  Petra Perner,et al.  The Problem of Normalization and a Normalized Similarity Measure by Online Data , 2011, Trans. Case Based Reason..

[56]  Isabelle Bichindaritz,et al.  Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice , 1998, EWCBR.

[57]  Padraig Cunningham,et al.  Emergent case-based reasoning applications , 2005, The Knowledge Engineering Review.

[58]  Stefan Wess,et al.  Case-Based and Symbolic Classification - A Case Study , 1993, EWCBR.

[59]  Petra Perner,et al.  A Novel Method for the Interpretation of Spectrometer Signals Based on Delta-Modulation and Similarity Determination , 2014, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.

[60]  Indriyati Atmosukarto,et al.  Similarity-Based Retrieval for Biomedical Applications , 2008, Case-Based Reasoning on Images and Signals.

[61]  Elena Zotenko,et al.  Characterizing Cell Types through Differentially Expressed Gene Clusters Using a Model-Based Approach , 2011, ICDM.

[62]  Ralph Bergmann,et al.  On the Role of Abstraction in Case-Based Reasoning , 1996, EWCBR.

[63]  Isabelle Bichindaritz,et al.  Memory Structures and Organization in Case-Based Reasoning , 2008, Case-Based Reasoning on Images and Signals.

[64]  Thomas Roth-Berghofer,et al.  Maintenance Memories: Beyond Concepts and Techniques for Case Base Maintenance , 2004, ECCBR.

[65]  Susan Craw,et al.  Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers , 2003, MLDM.

[66]  Petra Perner,et al.  Similarity Guided Learning of the Case Description and Improvement of the System Performance in an Image Classification System , 2002, ECCBR.

[67]  Isabelle Bichindaritz,et al.  Medical applications in case-based reasoning , 2005, The Knowledge Engineering Review.

[68]  Petra Perner,et al.  Using CBR Learning for the Low-Level and High-Level Unit of an Image Interpretation System , 1999 .