An Interval Approach to Discover Knowledge from Multiple Fuzzy Estimations

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An Interval Approach to Discover Knowledge from
Multiple Fuzzy Estimations

• Vagan Terziyan ,
• Seppo Puuronen, Helen Kaikova
• Department of Artificial Intelligence and
  Information Systems, Kharkov State Technical
  University of Radioelectronics, UKRAINE
• e-mail vagan_at_kture.cit-ua.net,
  vagan_at_jytko.jyu.fi
• Department of Computer Science and Information
  Systems, University of Jyvaskyla, FINLAND,
  e-mail sepi_at_jytko.jyu.fi
• GRWS98 - The 5-th Open German-Russian Workshop
  on Pattern Recognition and Image Understanding,
  Herrsching, Germany, 21-25 September, 1998

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Triangle of Friendship
Herrsching, GRWS98 Host
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Metaintelligence Laboratory Research Topics

• Knowledge and metaknowledge engineering
• Multiple experts
• Context in Artificial Intelligence
• Data Mining and Knowledge Discovery
• Temporal Reasoning
• Metamathematics
• Semantic Balance and Medical Applications
• Distance Education and Virtual Universities.

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Contents

• Context in Pattern Recognition
• Interval estimation
• Decontextualization with two intervals
• Decontextualization with several intervals
• Trends of uncertainty
• Interval estimation with several trends

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Context in Pattern Recognition
Context 1
recognition result
pattern
Context 2
Decontextualization
Context 3
Context 4
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Decontextualization of Noise in Pattern
Recognition with Multiple Estimations
estimations
noise
1
1
2
3
recognized pattern
2
pattern
Decontextualization
4
3
result
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The Problem of Interval Estimation

• Measurements (as well as expert opinions) are not
  absolutely accurate.
• The measurement result is expected to lie in the
  interval around the actual value.
• The inaccuracy leads to the need to estimate the
  resulting inaccuracy of data processing.
• When experts are used to estimate the value of
  some parameter, intervals are commonly used to
  describe degrees of belief.

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Noise of an Interval Estimation

• In many real life cases there is also some noise
  which does not allow direct measurement of
  parameters.
• The noise can be considered as an undesirable
  effect (context) to the evaluation of a
  parameter.
• Different measurement instruments as well as
  different experts possess different resistance
  against the influence of noise.
• Using measurements from several different
  instruments as well as estimations from multiple
  experts we try to discover the effect caused by
  noise and thus be able to derive the
  decontextualized measurement result.

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Basic Assumption

• The estimation of some parameter x given by more
  accurate knowledge source (i.e. source guarantees
  smaller upper bound of measurement error) is
  supposed to be closer to the actual value of
  parameter x (i.e. source is more resistant
  against a noise of estimation).
• The assumption allows us to derive different
  trends in cases when there are multiple
  estimations that result to shorter estimation
  intervals.

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Physical Interpretation of Decontextualization
R1
recognized pattern
real pattern
R2
Rres
real pattern
recognized pattern
Uncertainty is like a resistance for precise
recognition of a pattern
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Conclusion

• If you have several opinions (estimations,
  recognition results, solutions etc.) with
  different value of uncertainty you can select the
  most precise one,
• however
• it seems more reasonable to order opinions from
  the worst to the best one and try to recognize a
  trend of uncertainty which helps you to derive
  opinion more precise than the best one.