Journal of Intelligent Information Systems

, Volume 36, Issue 1, pp 73–98

A review and comparison of strategies for handling missing values in separate-and-conquer rule learning

Article

DOI: 10.1007/s10844-010-0121-8

Cite this article as:
Wohlrab, L. & Fürnkranz, J. J Intell Inf Syst (2011) 36: 73. doi:10.1007/s10844-010-0121-8

Abstract

In this paper, we review possible strategies for handling missing values in separate-and-conquer rule learning algorithms, and compare them experimentally on a large number of datasets. In particular through a careful study with data with controlled levels of missing values we get additional insights on the strategies’ different biases w.r.t. attributes with missing values. Somewhat surprisingly, a strategy that implements a strong bias against the use of attributes with missing values, exhibits the best average performance on 24 datasets from the UCI repository.

Keywords

Machine learningInductive rule learningMissing values

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Knowledge Engineering GroupTechnische Universität DarmstadtDarmstadtGermany