title: Multinomial logistic regression ensembles.
abstract: This article proposes a method for multiclass 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 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 two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (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 the random forest, support vector machines, and random multinomial logit model.
authors.affiliation: Department of Applied Mathematics and Statistics , Stony Brook University , Stony Brook , NY, USA.
authors.forename: Kyewon
authors.initials: K
authors.lastname: Lee
authors.forename: Hongshik
authors.initials: H
authors.lastname: Ahn
authors.forename: Hojin
authors.initials: H
authors.lastname: Moon
authors.forename: Ralph L
authors.initials: RL
authors.lastname: Kodell
authors.forename: James J
authors.initials: JJ
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000453:epidemiology; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D005544:Forecasting; D006471:Gastrointestinal Hemorrhage / Q000139:chemically induced / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D012372:ROC Curve; D011897:Random Allocation; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal