Ensemble learning for relational data

被引：0

作者：

Eldardiry, Hoda ^{[1
]}

Neville, Jennifer ^{[2
]}

Rossi, Ryan A. ^{[3
]}

机构：

[1] Department of Computer Science, Virginia Tech, 114 McBryde Hall, 225 Stanger Street, Blacksburg,VA,24061-0106, United States

[2] Department of Computer Science, Department of Statistics, Purdue University, 307 N. University Street, West Lafayette,IN,47907, United States

[3] Adobe Research, 345 Park Avenue, San Jose,CA,95110, United States

来源：

Journal of Machine Learning Research | 2020年 / 21卷

关键词：

We present a theoretical analysis framework for relational ensemble models. We show that ensembles of collective classifiers can improve predictions for graph data by reducing errors due to variance in both learning and inference. In addition; we propose a relational ensemble framework that combines a relational ensemble learning approach with a relational ensemble inference approach for collective classification. The proposed ensemble techniques are applicable for both single and multiple graph settings. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed framework. Finally; our experimental results support the theoretical analysis and confirm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both; are the best performers. © 2020 Hoda Eldardiry; Jennifer Neville and Ryan A. Rossi. License: CC-BY 4.0; see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v21/14-368.[!text type='html']html[!/text];

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