iryna yevseyeva niilo m ki institute university of jyv skyl iyevsev @cc jyu fi
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Faculty of Mathematical Information Technology University of Jyväskylä

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Iryna Yevseyeva Niilo Mäki Institute University of Jyväskylä iyevsev Faculty of Mathematical Information Technology University of Jyväskylä. Expert Classification with Multicriteria Decision Making methods. Main topics of presentation. Introduction to the classification problem

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iryna yevseyeva niilo m ki institute university of jyv skyl iyevsev @cc jyu fi
Iryna Yevseyeva

Niilo Mäki Institute

University of Jyväskylä

[email protected]

Faculty of Mathematical Information TechnologyUniversity of Jyväskylä

Expert Classification with Multicriteria Decision Making methods

main topics of presentation
Main topics of presentation
  • Introduction to the classification problem
  • Classification with different tools
  • Expert classification with Multicriteria decision making (MCDM) methods
  • NEURE project application
  • Conclusions and future research
  • Consists in assigning a set of alternatives evaluated on the number of criteria to one of the class.
  • Class can be predefined by:
  • profile - vectors of possible values or intervals of values for each class;
  • central reference object in each class.
possible tools for classification
Possible Tools for classification
  • Statistical models.
  • Different AI technique: Data Mining, Principal Component Analysis, Neural Networks, Expert systems.
  • MCDM analysis.
mcdm approaches to classification
MCDM Approaches to Classification
  • Trichotomic Segmentation (Moscarola J., Roy B., 1977).
  • ELECTRE Tri (Yu W.).
  • CLASS group of methods (Larichev O.I. et al.) : ORCLASS, CYCLE, SAC).
  • FINCLAS(Zopounidis C.).
expert classification
Expert Classification
  • Expert diagnostics: classification of the object to one of the predefined category or class based on the expert knowledge about properties of the object.
  • The decision reflects decision maker’s preferences expressed in the ordinal form.
  • Ordered classification.
expert classification1
Expert Classification
  • Cartesian production of criterion scales represents all possible alternatives.
  • Complete classification means defining every alternative in one class.
  • The best and the worst alternatives are defined in the first and the last classes consequently.
  • The rest of classification is organized through dialog between expert and system.
expert classification2
Expert Classification
  • Set of possible classes.
  • Set of possible criteria that describe object completely enough.
  • , where Set of scales with all possible criteria values.
  • , where Cartesian product of criterion values that provide complete set of all possible alternatives
expert classification3
Expert Classification
  • The information from DM defines an anti reflexive and transitive binary relations of strict preference (or dominance) on the set Y:
ordered classification
Ordered Classification
  • In case of ordinal classification the classes are ordered. That mean that for DM alternatives from the fist class is more preferable than alternative from the second one and so on. This reflect following binary relationship on set Y:
non contradictory classification
Non-contradictory classification
  • The classification is called non-contradictory if the following relations is fulfilled:
problems with expert classification
Problems with expert classification
  • The size of classification problems are usually large (direct classification of all possible alternatives by expert is impossible).
  • Possible errors in expert’s estimations.
classification with orclass and sac methods
Classification with ORCLASS and SAC methods
  • Classification is made during dialog with expert carried in natural language.
  • Construction of questionnaire with most “informative” question (indirect classification of less informative alternatives).
  • Checking of expert’s information for consistency.
  • Derivation of the Decision Rules for explanation of obtained decisions.
orclass and sac differences
ORCLASS and SAC Differences
  • SAC – Subset Alternative Classification (only real alternatives are taking into account).
  • Variation in the number of indirectly classified alternatives (standard deviation is taking into account).
sac algorithm
SAC Algorithm
  • The subset of alternatives that are not classified yet is defined. If it is empty , go to step 7.
  • For each element of defined subset the index of probability of alternative to be assigned in one of possible class is defined according to the formula:
  • For each element from subset the index of information is defined according to the formula:
sac algorithm1
SAC Algorithm
  • The alternative with maximum index of information is selected:
  • And presented to DM for classification;
  • In accordance with information obtained from DM the subset of possible classes for all non-classified alternatives is defined according to the formula:



  • The set of possible alternatives is classified.
neure problem area
NEURE Problem Area
  • Diagnostics of Attention-Deficit/Hyperactivity Disorder (ADHD).
  • persistent pattern of inattention and/or hyperactivity-impulsivity that is more frequent and severe than is typically observed in individuals at a comparable level of development.
  • Some of symptoms should be present before 7 years, although the diagnose has been made several years letter.
  • The symptoms must be presented in at least two settings.
  • Interference with appropriate social, academic or occupational functioning.
  • The symptoms occur together with other disorders.
current results
Current Results
  • Survey of MCDM methods for classification is done.
  • The algorithm of SAC was realized.
future direction of the work
Future direction of the work
  • Develop a GUI and explanation part for the expert system.
  • “Universality” of system for solving “all” possible problems.
  • Investigate new problem area ADHD.
  • Continue search on MCDM methods for classification tasks.
  • Continue comparative study of the applied MCDM methods.