---
title: "Minimum Splits Based Discretization for Continuous Features"
authors:
  - "Ke Wang"
  - "Han Chong Goh"
doi: ""
url: "https://ijcai.org/proceedings/1997/136"
pdf: "https://ijcai.org/proceedings/1997/0136.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "942-950"
track: "Learning"
keywords:
---

# Minimum Splits Based Discretization for Continuous Features

Ke Wang, Han Chong Goh

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 942-950.

## Abstract

Discretization refers to splitting the range of continuous values into intervals so as to provide useful information about classes. This is usually done by minimizing a goodness measure, subject to constraints such as the maximal number of intervals, the minimal number of examples per interval, or some stopping criterion for splitting. We take a different approach by searching for minimum splits that minimize the number of intervals with respect to a threshold of impurity (i.e., badness). We propose a "total entropy" motivated selection of the "best" split from minimum splits, without requiring additional constraints. Experiments show that the proposed method produces better decision trees.