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Knowledge Learning by Using Case Based Reasoning (CBR). Jun Yin and Yan Meng Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, USA. What’s CBR?.
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Knowledge Learning by Using Case Based Reasoning (CBR) Jun Yin and Yan Meng Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, USA
What’s CBR? • Case-Based Reasoning (CBR) is a name given to a reasoning method that solves a new problem by remembering a previous similar experiences and by reusing information and knowledge of that situation. • Ex: Medicine • doctor remembers previous patients especially for rare combinations of symptoms • Ex: Law • English/US law depends on precedence • case histories are consulted
CBR System Components • Case-base • database of previous cases (experience) • Retrieval of relevant cases • matching most similar case(s) • retrieving the solution(s) from these case(s) • Adaptation of solution • alter the retrieved solution(s) to reflect differences between new case and retrieved case(s)
Case Retrieval and Adaptation • Case retrieval • the process of finding within the case base those cases that are the closest to the current case. • Nearest Neighbor Retrieval • Inductive approaches • Knowledge Guided Approaches • Validated Retrieval • Case Adaptation • the process of translating the retrieved solution into the solution appropriate for the current problem.
Open Tools • freeCBR • is a free open source Java implementation of a "Case Based Reasoning" engine. (http://freecbr.sourceforge.net/) • myCBR • is an open-source case-based reasoning tool developed at DFKI. (http://mycbr-project.net/index.html)
freeCBR a very small case set:
freeCBR (cont.) search from the case set: the result of the search:
Open Tools – freeCBR& myCBR Modeling Similarity Measures: These two tools follow the approach in which, for an attribute-value based case representation consisting of n attributes, the similarity between a query q and a case c may be calculated as follows: Here, simi and wi denote the local similarity measure and the weight of attribute i, and Sim represents the global similarity measure.
Case Retrieval • Nearest Neighbor Retrieval • Retrieve most similar • k-nearest neighbor • - k-NN • - like scoring in bowls or curling • Example • 1-NN • 5-NN
Case Retrieval • Decision Tree • e.g. Case-Base indexedusing a decision-tree
Case Retrieval • We propose a self-organizing reservoir computing based network for case retrieval.
, Case Retrieval • Benchmark to evaluate the performance of proposed RC based network. • NARMA task • - The Nonlinear Auto-Regressive Moving Average (NARMA) task consists of modeling the output of the following tenth-order system :
NARMA task: Mean squared error = 0.128221, std = 0.0200301
Future Work • Integrate RC based network into CBR system • Develop the CBR system based on existing tools for more complicated tasks