Abstract
In the field of microarray data research, it is quite challenging to make classification due to small sample size and the high dimension of data. Moreover, the feature selection is crucial. In this paper, we propose multidimensional mutual information (MMI) feature selection method to select the most informative features for classification. After feature selection using the proposed MMI, Extreme Learning Machine (ELM) is used as an efficient classifier. So as to evaluate the performance of the proposed methodology, a typical dataset called Leukemia is selected to carry out a case study. Simulation results demonstrate the effectiveness of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 954-958 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538626184 |
| DOIs | |
| Publication status | Published - 30 Oct 2018 |
| Externally published | Yes |
| Event | 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, China Duration: 25 May 2018 → 27 May 2018 |
Publication series
| Name | Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 |
|---|
Conference
| Conference | 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 |
|---|---|
| Country/Territory | China |
| City | Enshi, Hubei Province |
| Period | 25/05/18 → 27/05/18 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Gene expression data
- classification
- extreme machine learning
- feature selection
- information gain
Fingerprint
Dive into the research topics of 'Effective cancer classification based on gene expression data using multidimensional mutual information and ELM'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver