NDLI: Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms

Content Provider	Springer Nature Link
Author	Sandri, Marco Zuccolotto, Paola
Copyright Year	2009
Abstract	Variable selection is one of the main problems faced by data mining and machine learning techniques. These techniques are often, more or less explicitly, based on some measure of variable importance. This paper considers Total Decrease in Node Impurity (TDNI) measures, a popular class of variable importance measures defined in the field of decision trees and tree-based ensemble methods, like Random Forests and Gradient Boosting Machines. In spite of their wide use, some measures of this class are known to be biased and some correction strategies have been proposed. The aim of this paper is twofold. Firstly, to investigate the source and the characteristics of bias in TDNI measures using the notions of informative and uninformative splits. Secondly, a bias-correction algorithm, recently proposed for the Gini measure in the context of classification, is extended to the entire class of TDNI measures and its performance is investigated in the regression framework using simulated and real data.
Starting Page	393
Ending Page	407
Page Count	15
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	20
Issue Number	4
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2009-05-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Impurity measures Ensemble learning Variable importance Artificial Intelligence (incl. Robotics) Mathematics Numeric Computing Statistics Statistics and Computing/Statistics Programs
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Model uncertainty and variable selection in Bayesian lasso regression

Stochastic matching pursuit for Bayesian variable selection

Importance tempering

Sequential importance sampling of binary sequences

Analysis and correction of bias in total decrease in node impurity measures for tree-based algorithms.

Stochastic boosting algorithms

A quasi-Newton acceleration for high-dimensional optimization algorithms

The ensemble Kalman filter is an ABC algorithm

Estimation of prediction error by using K-fold cross-validation

Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms

Similar Documents

Model uncertainty and variable selection in Bayesian lasso regression

Stochastic matching pursuit for Bayesian variable selection

Importance tempering

Sequential importance sampling of binary sequences

Analysis and correction of bias in total decrease in node impurity measures for tree-based algorithms.

Stochastic boosting algorithms

A quasi-Newton acceleration for high-dimensional optimization algorithms

The ensemble Kalman filter is an ABC algorithm

Estimation of prediction error by using K-fold cross-validation

Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms