Neighborhood-Hypernetwork for Classification of Imbalanced Data

DOI

10.2991/mmme-16.2016.52How to use a DOI?

Keywords

Imbalanced Dataset; Hypernetwork; hypergraph

Abstract

There exists several characteristics in imbalanced dataset, such as classes imbalance, between-class imbalance, overlapping, influenced noise, multi-classification with class imbalance, etc., which will greatly influence the classification performance of algorithms on imbalanced datasets. So far, the model has been widely used on many classification problems, such as DNA microarray data, text classification, stock prediction, and so on. As traditional hypernetwork cannot deal with continuous data directly and will bias to majority class when used on imbalanced data, this paper presents a neighborhood-hypernetwork model for classification of imbal-anced data. The paper improves the structure of hypernetwork to make sure it can deal with the issues men-tioned. The efficiency and advantage of the proposed approaches are verified by simulation experience on the UCI dataset.

Copyright

© 2016, 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  - J. Jiang
AU  - H.Q. Ran
AU  - K. Yang
PY  - 2016/10
DA  - 2016/10
TI  - Neighborhood-Hypernetwork for Classification of Imbalanced Data
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 225
EP  - 229
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.52
DO  - 10.2991/mmme-16.2016.52
ID  - Jiang2016/10
ER  -