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Cognitive Computation

Erschienen in:

01.06.2015

Uncertain Graph Classification Based on Extreme Learning Machine

verfasst von: Donghong Han, Yachao Hu, Shuangshuang Ai, Guoren Wang

Erschienen in: Cognitive Computation | Ausgabe 3/2015

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Abstract

The problem of graph classification has attracted much attention in recent years. The existing work on graph classification has only dealt with precise and deterministic graph objects. However, the linkages between nodes in many real-world applications are inherently uncertain. In this paper, we focus on classification of graph objects with uncertainty. The method we propose can be divided into three steps: Firstly, we put forward a framework for classifying uncertain graph objects. Secondly, we extend the traditional algorithm used in the process of extracting frequent subgraphs to handle uncertain graph data. Thirdly, based on Extreme Learning Machine (ELM) with fast learning speed, a classifier is constructed. Extensive experiments on uncertain graph objects show that our method can produce better efficiency and effectiveness compared with other methods.

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Titel: Uncertain Graph Classification Based on Extreme Learning Machine
verfasst von: Donghong Han
Yachao Hu
Shuangshuang Ai
Guoren Wang
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 3/2015
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-014-9295-7

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