Semi-supervised multi-graph classification using optimal feature selection and extreme learning machine
| Autor: | Yu Gu, Ge Yu, Jun Pang, Jia Xu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Cognitive Neuroscience Subgraph isomorphism problem Supervised learning Pattern recognition Feature selection 02 engineering and technology Graph Maximum common subgraph isomorphism problem Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Categorization Artificial Intelligence Graph classification 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) MathematicsofComputing_DISCRETEMATHEMATICS Extreme learning machine |
| Zdroj: | Neurocomputing. 277:89-100 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2017.01.114 |
| Popis: | A multi-graph is represented by a bag of graphs. Semi-supervised multi-graph classification is a partly supervised learning problem, which has a wide range of applications, such as bio-pharmaceutical activity tests, scientific publication categorization and online product recommendation. However, to the best of our knowledge, few research works have be reported. In this paper, we propose a semi-supervised multi-graph classification algorithm to handle the semi-supervised multi-graph classification problem. Our algorithm consists of three main steps, including the optimal subgraph feature selection, the subgraph feature representation of multi-graph and the semi-supervised classifier building. We first propose an evaluation criterion of the optimal subgraph features, which not only considers unlabeled multi-graphs but also considers the constraints between the multi-graph level and the graph level. Then, the optimal subgraph feature selection problem is equivalently converted into the problem of mining m most informative subgraph features. Based on those derived m subgraph features, every multi-graph is represented by an m -dimensional vector, where the i th dimension equals to 1 if at least one graph involved in the multi-graph contains the i th subgraph feature. At last, based on these vectors, semi-supervised extreme learning machine(semi-supervised ELM) is adopted to build the prediction model for predicting the labels of unseen multi-graphs. Extensive experiments on real-world and synthetic graph datasets show that the proposed algorithm is effective and efficient. |
| Databáze: | OpenAIRE |
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