An Evolutionary Algorithm for Making Decision Graphs for Classification Problems

DOI

10.2991/jrnal.2016.3.1.11How to use a DOI?

Keywords

evolutionary computation, decision graph, classification, majority vote, multi root nodes

Abstract

As the exponential increase of data in the world, machine learning, pattern recognition, data mining etc. are attracting more attentions recently. Classification is one of the major research in pattern recognition and a large number of methods have been proposed such as decision trees, neural networks (NNs), support vector machines (SVMs). In order to easily understand and analyze the reason of the classification results, decision trees are useful comparing to NNs and SVMs. In this paper, to enhance the classification ability of decision trees, a new evolutionary algorithm for creating decision graphs is proposed as a superset of decision trees, where multi-root nodes and majority voting mechanism based on Maximum a posteriori are introduced. In the performance evaluation, it is clarified that the proposed method shows better classification ability than decision trees.

Copyright

© 2013, 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  - JOUR
AU  - Shingo Mabu
AU  - Masanao Obayashi
AU  - Takashi Kuremoto
PY  - 2016
DA  - 2016/06/01
TI  - An Evolutionary Algorithm for Making Decision Graphs for Classification Problems
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 45
EP  - 49
VL  - 3
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2016.3.1.11
DO  - 10.2991/jrnal.2016.3.1.11
ID  - Mabu2016
ER  -