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

Authors

  • Lars Wohlrab
    • Knowledge Engineering GroupTechnische Universität Darmstadt
    • Knowledge Engineering GroupTechnische Universität Darmstadt
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 learning Inductive rule learning Missing values

Copyright information

© Springer Science+Business Media, LLC 2010