Result: Multi-Step Classification Trees

Title:
Multi-Step Classification Trees
Authors:
CHANG, Youngjae
Source:
Communications in statistics. Simulation and computation. 41(8-10):1728-1744
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Combinatoire. Structures ordonnées, Combinatorics. Ordered structures, Combinatoire, Combinatorics, Théorie des graphes, Graph theory, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Processus stochastiques, Stochastic processes, Statistiques, Statistics, Analyse multivariable, Multivariate analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Ajustement, Fitting, Ajuste, Algorithme, Algorithm, Algoritmo, Analyse discriminante, Discriminant analysis, Análisis discriminante, Analyse multivariable, Multivariate analysis, Análisis multivariable, Arbre décision, Decision tree, Arbol decisión, Estimation statistique, Statistical estimation, Estimación estadística, Forêt, Forests, Bosque, Méthode statistique, Statistical method, Método estadístico, Méthode à pas, Step method, Método a paso, Processus stochastique, Stochastic process, Proceso estocástico, Prédiction, Prediction, Predicción, Simulation numérique, Numerical simulation, Simulación numérica, Théorie filtrage, Filtering theory, Théorie prédiction, Prediction theory, 05C05, 60G25, 62H30, 62M20, Classification automatique(statistiques), Mauvaise classification, Bad classification, Sélection variable, Variable selection, 62C12, CRUISE, Classification tree, GUIDE
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Research Department, The Bank of Korea, Seoul, Korea, Republic of
ISSN:
0361-0918
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26164019
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.26164019
Database:
PASCAL Archive

Further Information

Many algorithms originated from decision trees have been developed for classification problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy, namely high misclassification rates when there are many irrelevant variables. We propose multi-step classification trees with adaptive variable selection (the multi-step GUIDE classification tree (MG) and the multi-step CRUISE classification tree (MC) to handle this problem. The variable selection step and the fitting step comprise the multi-step method. We compare the performance of classification trees in the presence of irrelevant variables. MG and MC perform better than Random Forest and C4.5 with an extremely noisy dataset. Furthermore, the prediction accuracy of our proposed algorithm is relatively stable even when the number of irrelevant variables increases, while that of other algorithms worsens.