Name
Title | ||
|---|---|---|
Feature Selection for Medical Diagnosis Using Fuzzy Artmap Classification and Intersection Conflict |
Abstract | ||
|---|---|---|
Studying complex systems including biological systems is a multi-disciplinary research area. It must be derived by the recent explosion of ICT including high-performance computing, high-throughput experiments, the Internet, knowledge discovery and Artificial Intelligence (AI). The goal of this research is to establish a computational architecture and tools to deal with complex systems based on such advanced technologies. Therefore in the case of medical diagnosis based on machine learning model, we need to reduce the number of variables according with their relevance and allowing to take decisions in real-time. This approach is realized in two stages. In the first one, we classify the unfaulty functioning data of system using the fuzzy-ARTMAP classification. In the second stage, a conflict is accounted between features of test data based on the hyper-cubes resulted in the first stage. Two features are in conflict if her intersection does not belong to the model elaborated by fuzzy-ARTMAP classification. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WAINA.2010.83 | Advanced Information Networking and Applications Workshops |
Keywords | Field | DocType |
multi-disciplinary research area,fuzzy artmap classification,feature selection,artificial intelligence,complex system,high-performance computing,advanced technology,test data,high-throughput experiment,biological system,fuzzy-artmap classification,computational architecture,intersection conflict,computer architecture,high throughput,fault detection,real time,artificial intelligent,high performance computing,intelligent sensors,knowledge discovery,encoding,fuzzy systems,complex systems,learning artificial intelligence,adaptive resonance theory,application software,artificial neural networks,biological systems,classification algorithms,internet,medical diagnosis,feature extraction,hypercubes,machine learning,ict,breast cancer | Feature selection,Computer science,Fuzzy logic,Feature extraction,Artificial intelligence,Knowledge extraction,Fuzzy control system,Artificial neural network,Statistical classification,Medical diagnosis,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-6701-3 | 1 | 0.35 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mourad Benkaci | 1 | 3 | 2.44 |
| Bruno Jammes | 2 | 3 | 1.10 |
| Doncescu, A. | 3 | 86 | 25.70 |