期刊
APPLIED SOFT COMPUTING
卷 70, 期 -, 页码 1146-1153出版社
ELSEVIER
DOI: 10.1016/j.asoc.2017.09.020
关键词
Random forest; Oblique random forest; Neural network; Random vector functional link network (RVFL); Classification; Ensemble
Ensembles of decision trees and neural networks are popular choices for solving classification and regression problems. In this paper, a new ensemble of classifiers that consists of decision trees and random vector functional link network is proposed for multi-class classification. The random vector functional link network (RVFL) partitions the original training samples into K distinct subsets, where Kis the number of classes in a data set, and a decision tree is induced for each subset. Both univariate and multivariate (oblique) decision trees are used with RVFL. The performance of the proposed method is evaluated on 65 multi-class UCI datasets. The results demonstrate that the classification accuracy of the proposed ensemble method is significantly better than other state-of-the-art classifiers for medium and large sized data sets. (C) 2017 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据