Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

Details Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

2012-04-19

arxiv.org/abs/1204.4329v1

International Conference on Knowledge and Systems Engineering, Hanoi : Viet Nam (2010)

Computer Science

Learning

Scientific paper

Nowadays, supervised learning is commonly used in many domains. Indeed, many works propose to learn new knowledge from examples that translate the expected behaviour of the considered system. A key issue of supervised learning concerns the description language used to represent the examples. In this paper, we propose a method to evaluate the feature set used to describe them. Our method is based on the computation of the consistency of the example base. We carried out a case study in the domain of geomatic in order to evaluate the sets of measures used to characterise geographic objects. The case study shows that our method allows to give relevant evaluations of measure sets.

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