Discretization

Abstract

Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. An overview of discretization together with a complete outlook and taxonomy are supplied in Sects. 9.1 and 9.2. We conduct an experimental study in supervised classification involving the most representative discretizers, different types of classifiers, and a large number of data sets (Sect. 9.4).

Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

“…In order to train the Maximum Entropy model with a very limited training dataset, we need to convert attributes that have continuous numeric values into discrete ones. There has been a lot of research done on continuous feature discretization field [27][28][29][30][31][32]. Methods for discretization are broadly classified into Supervised vs. Unsupervised, Global vs. Local, and Static vs.…”