Incremental Maximum Gaussian Mixture Partition For Classification

DOI

10.2991/jimec-17.2017.31How to use a DOI?

Keywords

Classification, Gaussian Function, K-means

Abstract

In the field of classification, the main task of most algorithms is to find a perfect decision boundary. However, most decision boundaries are too complex to be discovered directly. Therefore, in this paper, we proposed an Incremental Maximum Gaussian Mixture Partition (IMGMP) algorithm for classification, aiming to solve those problems with complex decision boundaries. As a self-adaptive algorithm, it uses a divide and conquer strategy to calculate out a reasonable decision boundary by step. An Improved K-means clustering and a Maximum Gaussian Mixture model are used in the classifier. This algorithm also has been tested on artificial and real-life datasets in order to evaluate its remarkable flexibility and robustness.

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  - Xianbin Hong
AU  - Jiehao Zhang
AU  - Sheng-Uei Guan
AU  - Di Yao
AU  - Nian Xue
AU  - Xuan Zhao
AU  - Xin Huang
PY  - 2017/10
DA  - 2017/10
TI  - Incremental Maximum Gaussian Mixture Partition For Classification
BT  - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017)
PB  - Atlantis Press
SP  - 141
EP  - 144
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimec-17.2017.31
DO  - 10.2991/jimec-17.2017.31
ID  - Hong2017/10
ER  -