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Invited Talks
- Bernard De Baets, Ghent University, Belgium
Title: Monotone, but not boring: how to deal with reversed preference in monotone classification.
Abstract: We deal with a particular type of classification problems, in which there
exists a linear ordering on the label set (as in ordinal regression) as well
as on the domain of each of the features. Moreover, there exists a monotone
relationship between the features and the class labels. Such problems
of monotone classification typically arise in a multi-criteria
evaluation setting.
When learning such a model from a data set, we are confronted with
data impurity in the form of reversed preference. We present the
Ordinal Stochastic Dominance Learner framework which allows to build
various instance-based algorithms able to process such data. Moreover,
we explain how reversed preference can be eliminated by relating this
problem to the maximum independent set problem and solving it
efficiently using flow network algorithms.
- Arie Ben-David, Holon Institute of Technology, Israel
Title: Monotone Ordinal Concept Learning: Past, Present and Future.
Abstract: This talk will survey the history of ordinal concept learning in general and that of monotone ordinal learning in particular.
Some approaches that were taken over the years will be presented, as well as a survey of recent publications about the topic.
Some key points that should be addressed in future research will be discussed. In particular: the use of a standard operating
environment, the establishment of a "large enough" publicly available body of ordinal benchmarking files, and the use of an
agreed upon set of meters and procedures for comparing the performance of the various models.
Time will be allocated for an open discussion about how these and other goals can be promoted.
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