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A Comparison of Strategies for Handling Missing Values in Rule Learning

Wohlrab, Lars and Fürnkranz, Johannes (2009):
A Comparison of Strategies for Handling Missing Values in Rule Learning.
[Online-Edition: http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-20...],
[Report]

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.

Item Type: Report
Erschienen: 2009
Creators: Wohlrab, Lars and Fürnkranz, Johannes
Title: A Comparison of Strategies for Handling Missing Values in Rule Learning
Language: English
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.

Divisions: 20 Department of Computer Science > Knowl­edge En­gi­neer­ing
20 Department of Computer Science
Date Deposited: 24 Jun 2011 14:43
Official URL: http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-20...
Identification Number: TUD-KE-2009-03
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