FUSINTER: a method for discretization of continuous attributes
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Investigation and Reduction of Discretization Variance in Decision Tree Induction
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Dynamic discreduction using Rough Sets
Applied Soft Computing
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Many supervised induction algorithms require discrete data, however real data often comes in both discrete and continuous formats. Quality discretization of continuous attributes is an important problem that has effects on accuracy, complexity, variance and understandability of the induction model. Usually, discretization and other types of statistical processes are applied to subsets of the population as the entire population is practically inaccessible. For this reason we argue that the discretization performed on a sample of the population is only an estimate of the entire population. Most of the existing discretization methods, partition the attribute range into two or several intervals using a single or a set of cut points. In this paper, we introduce two variants of a resampling technique (such as bootstrap) to generate a set of candidate discretization points and thus, improving the discretization quality by providing a better estimation towards the entire population. Thus, the goal of this paper is to observe whether this type of resampling can lead to better quality discretization points, which opens up a new paradigm to construction of soft decision trees.