Abstract
Existing propositionalisation approaches mainly deal with categorical attributes. Few approaches deal with continuous attributes. A first solution is then to discretise numeric attributes to transform them into categorical ones. Alternative approaches dealing with numeric attributes consist in aggregating them with simple functions such as average, minimum, maximum, etc. We propose an approach dual to discretisation that reverses the processing of objects and thresholds, and whose discretisation corresponds to quantiles. Our approach is evaluated thoroughly on artificial data to characterize its behaviour with respect to two attribute-value learners, and on real datasets.
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El Jelali, S., Braud, A., Lachiche, N. (2013). Propositionalisation of Continuous Attributes beyond Simple Aggregation. In: Riguzzi, F., Železný, F. (eds) Inductive Logic Programming. ILP 2012. Lecture Notes in Computer Science(), vol 7842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38812-5_3
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DOI: https://doi.org/10.1007/978-3-642-38812-5_3
Publisher Name: Springer, Berlin, Heidelberg
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