TY - JOUR
T1 - Some features speak loud, but together they all speak louder
T2 - A study on the correlation between classification error and feature usage in decision-tree classification ensembles
AU - Cervantes, Bárbara
AU - Monroy, Raúl
AU - Medina-Pérez, Miguel Angel
AU - Gonzalez-Mendoza, Miguel
AU - Ramirez-Marquez, Jose
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/1
Y1 - 2018/1
N2 - While diversity has been argued to be the rationale for the success of an ensemble of classifiers, little has been said on how uniform use of the feature space influences classification error. Following an observation from a recent result, published elsewhere, among several ensembles of decision trees, those with a more uniform feature-use frequency also have a smaller classification error. This paper provides further support to such hypothesis. We have conducted experiments over 60 classification datasets, using 42 different types of decision tree ensembles, to test our hypothesis. Our results validate the hypothesis, prompting the design of ensemble construction methods that make a more uniform use of features, for classification problems of low and medium dimensionality.
AB - While diversity has been argued to be the rationale for the success of an ensemble of classifiers, little has been said on how uniform use of the feature space influences classification error. Following an observation from a recent result, published elsewhere, among several ensembles of decision trees, those with a more uniform feature-use frequency also have a smaller classification error. This paper provides further support to such hypothesis. We have conducted experiments over 60 classification datasets, using 42 different types of decision tree ensembles, to test our hypothesis. Our results validate the hypothesis, prompting the design of ensemble construction methods that make a more uniform use of features, for classification problems of low and medium dimensionality.
KW - Decision tree ensemble
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U2 - 10.1016/j.engappai.2017.10.007
DO - 10.1016/j.engappai.2017.10.007
M3 - Article
AN - SCOPUS:85032672851
SN - 0952-1976
VL - 67
SP - 270
EP - 282
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
ER -