Type
Text
Type
Dissertation
Advisor
Nancy Mendell | Marsh, Steve | Haipeng Xing | Sangjin Hong.
Date
2010-08-01
Keywords
Statistics | classification, ensemble method, multinomial logistic regression model
Department
Department of Applied Mathematics and Statistics
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/72577
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
This research proposes a method for multi-way classification problems using ensembles 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 without variable selection. By combining multiple models the proposed method can handle a huge database without a parametric constraint needed for analyzing high-dimensional data,and the random partition can improve the prediction accuracy by reducing the correlation 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 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. Performance of the proposed model is compared to a single multinomial logit model and another ensemble method combining multinomial logit models using the algorithm of Random Forest. The proposed model shows a substantial improvement in overall prediction accuracy over a multinomial logit model.
Recommended Citation
Lee, Kyewon, "A Multi-class Classification using Ensembles of Multinomial Logistic Regression Models" (2010). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 1781.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/1781