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DECIDE
DECision aId and knowleDge discovEry
Aims and objectives throughout the project
Project Leader :
Philippe Lenca
Department:
• Logics in Uses,
Social Science
and Information Science
Project team:
Philippe Lenca,
Laurent Brisson,
Patrick Meyer,
Sorin Moga,
Gilles Coppin
Yannick Le Bras,
Thomas Veneziano,
Gurvan Uguen,
Thanh Nghi Do,
Komate Amphawan.
The aim of the DECIDE (DECision aId and knowleDge discovEry) project is to
develop decision support systems to help making robust decisions of high
quality and integrating the different actors of the decision making process.
This aid to decision makers takes two forms: the first one relies on strategies
made by the decision makers (consisting among other things of supplying a
mirror of expertise), the second one consists of strategies the decision
makers could set up (consisting among other things of learning these
strategies). Therefore the team studied methodological and algorithmic
aspects (complete and heuristic) as well as robustness issues models
(multicriteria, naturalistic) for decision aid and data mining.
Our ambition was to develop systems which could help the expert and nonexpert.
The naturalistic approach consists of modelling the behaviour of the
expert decision maker with an as much as possible, reduced set of reference
decision structures. These form the mirror of expertise and once expressed
in adequate form (mainly in the form of production rules) make up the
foundation of (cognitive) systems for (intrusive or non-intrusive) decisionmaking.
From this foundation, more realistic and closer to the decision maker,
our ambition is to develop cognitive models for decision making on an
individual and collective level. When faced with non-expert decision makers
it is vital to help them to express their system of preferences (notably decision
criteria, relative importance of these criteria, etc). Our work on mathematical
modelling, to help multicriteria decision-making, leans towards this point and
the development of constructive approaches. Moreover, we also looked at the
robustness of solutions proposed. We looked at the two great paradigms,
generally studied separately in decision-making, outranking approaches and
utility based theory.
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In data mining our aims are to improve mining algorithms and to help the
evaluation and interpretation of results. Indeed, it is generally agreed that one
of the key stages in the mining process is the evaluation and interpretation
phase. This work covered two areas. We studied the design and analysis of
new learning algorithms, which improved the reference performance, and the
validation of extracted knowledge. For algorithms, we mainly studied the
integration of objective measures allowing both to develop efficient algorithms
(complete and heuristic) and guarantee the results provided. This work is done
for algorithms which produce production rules and which are the most used,
notably the association rules (non-supervised learning), the class association
rules and decision trees (supervised learning). This approach is completed by
the integration of expert knowledge into the algorithms and by the
aggregation of methods which allow to build a set of classifiers. For validation
of extracted knowledge our ambition is to supply a generic framework of the
notion of quality. To do this we propose to formalise the properties of
interestingness measures and propose to develop a methodology to study
these measures of quality in order to supply the decision maker with help in
selecting the good knowledge. Once again this approach is completed by the
integration of expert knowledge in order to define the subjective measures of
quality.