On Improving Discretization Quality by a Bagging Technique

Most of the real data often comes in a mixed format (discrete or continuous), however many supervised induction algorithms require discrete data. Quality discretization of continuous attributes is an important problem that has effects on accuracy, complexity, variance and understandability of the induction models. 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 a resampling based bagging technique (using 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 bagging variant can lead to better discretization points.