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];

D O I：

暂无

中图分类号：

学科分类号：

摘要：

引用

共 50 条

[21] Transforming Graph Data for Statistical Relational Learning
Rossi, Ryan A.
McDowell, Luke K.
Aha, David W.
Neville, Jennifer
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2012, 45 : 363 - 441
[22] Dimensionality Reduction in Data Summarization Approach to Learning Relational Data
Kheau, Chung Seng
Alfred, Rayner
Keng, Lau Hui
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT I,, 2013, 7802 : 166 - 175
[23] Big data classification of learning behaviour based on data reduction and ensemble learning
Wang, Taotao
Wu, Xiaoxuan
INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING, 2023, 33 (4-5) : 496 - 510
[24] Iterative ensemble classification for relational data: A case study of semantic web services
Hess, A
Kushmerick, N
MACHINE LEARNING: ECML 2004, PROCEEDINGS, 2004, 3201 : 156 - 167
[25] An Ensemble Learning Approach for Data Stream Clustering
Fathzadeh, Ramin
Mokhtari, Vahid
2013 21ST IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2013,
[26] A Survey on Ensemble Learning for Data Stream Classification
Gomes, Heitor Murilo
Barddal, Jean Paul
Enembreck, Fabricio
Bifet, Albert
ACM COMPUTING SURVEYS, 2017, 50 (02)
[27] Ensemble learning for data stream analysis: A survey
Krawczyk, Bartosz
Minku, Leandro L.
Gama, Joao
Stefanowski, Jerzy
Wozniak, Michal
INFORMATION FUSION, 2017, 37 : 132 - 156
[28] Droplet Ensemble Learning on Drifting Data Streams
Loeffel, Pierre-Xavier
Bifet, Albert
Marsala, Christophe
Detyniecki, Marcin
ADVANCES IN INTELLIGENT DATA ANALYSIS XVI, IDA 2017, 2017, 10584 : 210 - 222
[29] Incremental learning of ensemble classifiers on ECG data
Macek, J
18TH IEEE SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS, 2005, : 315 - 320
[30] Learning of classifier ensemble using virtual data
Jang, M
Cho, S
IC-AI'2000: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 1-III, 2000, : 955 - 959

← 1 2 3 4 5 →