Wohlrab, Lars ; Fürnkranz, Johannes (2009)
A Comparison of Strategies for Handling Missing Values in Rule Learning.
Report, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Wohlrab, Lars ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | A Comparison of Strategies for Handling Missing Values in Rule Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| URL / URN: | http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-20... |
| Kurzbeschreibung (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. |
| ID-Nummer: | TUD-KE-2009-03 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik > Knowledge Engineering 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 24 Jun 2011 14:43 |
| Letzte Änderung: | 05 Mär 2013 09:49 |
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