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Multinomial Logistic Regression Ensembles This article proposes a method for multiclass classification problems using ensem-
 

Summary: Multinomial Logistic Regression Ensembles
Abstract
This article proposes a method for multiclass classification problems using ensem-
bles of multinomial logistic regression models. A multinomial logit model is used as
a base classifier in ensembles from random partitions of predictors. The multinomial
logit model can be applied to each mutually exclusive subset of the feature space with-
out variable selection. By combining multiple models the proposed method can handle
a huge database without a constraint needed for analyzing high-dimensional data, and
the random partition can improve the prediction accuracy by reducing the correla-
tion among base classifiers. The proposed method is implemented using R and the
performance including overall prediction accuracy, sensitivity, and specificity for each
category is evaluated on two real data sets and simulation data sets. To investigate
the quality of prediction in terms of sensitivity and specificity, area under the ROC
curve (AUC) is also examined. The performance of the proposed model is compared
to a single multinomial logit model and it shows a substantial improvement in overall
prediction accuracy. The proposed method is also compared with other classification
methods such as Random Forest, Support Vector Machines, and Random Multinomial
Logit Model.
Keywords: Class prediction; Ensemble; Logistic regression; Majority voting; Multi-
nomial logit; Random partition.

  

Source: Ahn, Hongshik - Department of Applied Mathematics and Statistics, SUNY at Stony Brook

 

Collections: Materials Science