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.
['Mourad Benkaci', 'Bruno Jammes', 'Andrei Doncescu']
Feature Selection for Medical Diagnosis Using Fuzzy Artmap Classification and Intersection Conflict