Name
Abstract | ||
|---|---|---|
Biological network classification is an eminently challenging task in the domain of data mining since the networks contain complex structural information. Conventional biochemical experimental methods and the existing intelligent algorithms still suffer from some limitations such as immense experimental cost and inferior accuracy rate. To solve these problems, in this paper, we propose a novel framework for Biological graph classification named Biogc, which is specifically developed to predict the label of both small-scale and large-scale biological network data flexibly and efficiently. Our framework firstly presents a simplified graph kernel method to capture the structural information of each graph. Then, the obtained informative features are adopted to train different scale biological network data-oriented classifiers to construct the prediction model. Extensive experiments on five benchmark biological network datasets on graph classification task show that the proposed model Biogc outperforms the state-of-the-art methods with an accuracy rate of 98.90% on a larger dataset and 99.32% on a smaller dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3233/IDA-205240 | INTELLIGENT DATA ANALYSIS |
Keywords | DocType | Volume |
Biological network, graph classification, machine learning, graph kernel, deep neural network | Journal | 25 |
Issue | ISSN | Citations |
5 | 1088-467X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bentian Li | 1 | 0 | 0.68 |
| De-Chang Pi | 2 | 177 | 39.40 |
| Yunxia Lin | 3 | 2 | 1.39 |
| Izhar Ahmed Khan | 4 | 7 | 3.49 |