A Parallel Decision Tree Based Algorithm on MPI for Multi-label Classification Learning

DOI

10.2991/caai-17.2017.83How to use a DOI?

Keywords

multi-label classification; decision tree; parallelization; MPI

Abstract

Multi-label classification is an important area of data mining, in where decision tree is one of the effective means to solve the problem. It faced a huge challenge of performance caused by large size of data. First, we translate the multi-label classification to several binary classifications. Then we analyzed the potential parallelism of decision tree based multi-label classification algorithm from four parts and overall applied them in the training and predicting phases. The parallel algorithm was implemented with MPI and the performance of parallel decision tree based multi-label classification algorithm is analyzed and compared program designations and experiments, which demonstrate that our parallel algorithm could improve the computing efficiency and still has some extensibilities.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yihao Zhou
AU  - Zhenzhou Ji
AU  - Kaiyu Wang
PY  - 2017/06
DA  - 2017/06
TI  - A Parallel Decision Tree Based Algorithm on MPI for Multi-label Classification Learning
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 366
EP  - 369
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.83
DO  - 10.2991/caai-17.2017.83
ID  - Zhou2017/06
ER  -