Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
On Multi-class Problems and Discretization in Inductive Logic Programming
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
The use of a Bayesian network for web effort estimation
ICWE'07 Proceedings of the 7th international conference on Web engineering
Predicting web development effort using a bayesian network
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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Numeric data has traditionally received little attention in the field of Multi-Relational Data Mining (MRDM). It is often assumed that numeric data can simply be turned into symbolic data by means of discretisation. However, very few guidelines for successfully applying discretisation in MRDM exist. Furthermore, it is unclear whether the loss of information involved is negligible. In this paper, we consider different alternatives for dealing with numeric data in MRDM. Specifically, we analyse the adequacy of discretisation by performing a number of experiments with different existing discretisation approaches, and comparing the results with a procedure that handles numeric data dynamically. The discretisation procedures considered include an algorithm that is insensitive to the multi-relational structure of the data, and two algorithms that do involve this structure. With the empirical results thus obtained, we shed some light on the applicability of both dynamic and static procedures (discretisation), and give recommendations for when and how they can best be applied.