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Knowledge Learning by Using Case Based Reasoning (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 – PowerPoint PPT presentation

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Title: Knowledge Learning by Using Case Based Reasoning (CBR)


1
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
2
Whats 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

3
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)

4
The Case Based Reasoning Cycle
5
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.

6
Open Tools
  • freeCBR
  • is a free open source Java implementation of a
    "Case Based Reasoning" engine. (http//freecbr.sou
    rceforge.net/)
  • myCBR
  • is an open-source case-based reasoning tool
    developed at DFKI. (http//mycbr-project.net/index
    .html)

7
freeCBR
a very small case set
8
freeCBR (cont.)
search from the case set
the result of the search
9
Open Tool myCBR
10
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.
11
Case Retrieval
  • Nearest Neighbor Retrieval
  • Retrieve most similar
  • k-nearest neighbor
  • - k-NN
  • - like scoring in bowls or curling
  • Example
  • 1-NN
  • 5-NN

12
Case Retrieval
  • Decision Tree
  • e.g.

Case-Base indexed using a decision-tree
13
Case Retrieval
  • We propose a self-organizing reservoir
    computing based network for case retrieval.

14
,
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

15
NARMA task
Mean squared error 0.128221, std 0.0200301
16
  • Future Work
  • Integrate RC based network into CBR system
  • Develop the CBR system based on existing tools
    for more complicated tasks
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