Arbeitspapier
Variable selection bias in classification trees based on imprecise probabilities
Classification trees based on imprecise probabilities provide an advancement of classical classification trees. The Gini Index is the default splitting criterion in classical classification trees, while in classification trees based on imprecise probabilities, an extension of the Shannon entropy has been introduced as the splitting criterion. However, the use of these empirical entropy measures as split selection criteria can lead to a bias in variable selection, such that variables are preferred for features other than their information content. This bias is not eliminated by the imprecise probability approach. The source of variable selection bias for the estimated Shannon entropy, as well as possible corrections, are outlined. The variable selection performance of the biased and corrected estimators are evaluated in a simulation study. Additional results from research on variable selection bias in classical classification trees are incorporated, implying further investigation of alternative split selection criteria in classification trees based on imprecise probabilities. Keywords. Classification trees ; credal classification ; variable selection bias ; attribute selection error ; Shannon entropy ; entropy estimation
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 419
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Strobl, Carolin
- Ereignis
-
Veröffentlichung
- (wer)
-
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (wo)
-
München
- (wann)
-
2005
- DOI
-
doi:10.5282/ubm/epub.1788
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1788-0
- Letzte Aktualisierung
-
10.03.2025, 11:46 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Strobl, Carolin
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Entstanden
- 2005
Ähnliche Objekte (12)
Statistical sources of variable selection bias in classification tree algorithms based on the Gini index
The Classification of Continuation Probabilities
Unbiased split selection for classification trees based on the Gini Index
Testing for Selection Bias
Classification and regression trees
Classification and Regression Trees
Takeover Times and Probabilities of Non-Generational Selection Rules
Investigating topic bias in emotion classification
Selection bias in historical housing data
Bias-adjusted treatment effects under equal selection
Recursive Portfolio Selection with Decision Trees
Recursive portfolio selection with decision trees
Statistical sources of variable selection bias in classification tree algorithms based on the Gini index
The Classification of Continuation Probabilities
Unbiased split selection for classification trees based on the Gini Index
Testing for Selection Bias
Classification and regression trees
Classification and Regression Trees
Takeover Times and Probabilities of Non-Generational Selection Rules
Investigating topic bias in emotion classification
Selection bias in historical housing data
Bias-adjusted treatment effects under equal selection
Recursive Portfolio Selection with Decision Trees
Recursive portfolio selection with decision trees
Statistical sources of variable selection bias in classification tree algorithms based on the Gini index
The Classification of Continuation Probabilities
Unbiased split selection for classification trees based on the Gini Index
Testing for Selection Bias
Classification and regression trees
Classification and Regression Trees
Takeover Times and Probabilities of Non-Generational Selection Rules
Investigating topic bias in emotion classification
Selection bias in historical housing data
Bias-adjusted treatment effects under equal selection
Recursive Portfolio Selection with Decision Trees