A PSO-based Generic Classifier Design and Weka Implementation Study

DOI

10.2991/ifmca-16.2017.4How to use a DOI?

Keywords

Classifier; PSO algorithm; Weka platform; instance-based learning.

Abstract

Aiming at dataset with different feature attributes and multi nominal decision categories, a new design algorithm using PSO for instance-based learning classifier is put forward which can facilitate to make a better classification decision. On the basis of classes and interfaces in Weka platform, this improved algorithm is implemented and added in the platform. Taking two datasets as for test cases, not only is the classifier compared with optimizing different objective functions, but also is evaluated with other classifiers. Comparisons demonstrate that the proposed algorithm can get more effective classifying results and maybe considered a generic classifier.

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  - Hui Hu
AU  - Xiaodong Mao
AU  - Qin Xi
PY  - 2017/03
DA  - 2017/03
TI  - A PSO-based Generic Classifier Design and Weka Implementation Study
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 27
EP  - 31
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.4
DO  - 10.2991/ifmca-16.2017.4
ID  - Hu2017/03
ER  -