Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a new approach in machine learning, es- pecially, in supervised classification and reasoning under uncertainty. For many classification problems, uncertainty is often inherent in modeling applications and should be treated carefully and not rejected in order to make better decisions. Artificial Immune Recognition System (AIRS) is a ll known classifier that has provided good results in the certain context. Hover, this method is not able to cope with uncertainty. In order to overcome this limitation,  propose a new classification ap- proach combining the AIRS and possibility theory. The new approach is allowing to deal with uncertain attribute values of training instances. The uncertainty is expressed via possibility distributions. Experimenta- tions on real data sets from the U.C.I machine learning repository show good performances of the proposed approach.
['Rim Hentech', 'Ilyes Jenhani', 'Zied Elouedi']
I am writing a research paper on a idea Learning from Uncertain Data Using Possibilistic Artificial Immune Recognition Systems
[{"from": "human", "value": "I am writing a research paper on a idea Learning from Uncertain Data Using Possibilistic Artificial Immune Recognition Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a new approach in machine learning, es- pecially, in supervised classification and reasoning under uncertainty. For many classification problems, uncertainty is often inherent in modeling applications and should be treated carefully and not rejected in order to make better decisions. Artificial Immune Recognition System (AIRS) is a ll known classifier that has provided good results in the certain context. Hover, this method is not able to cope with uncertainty. In order to overcome this limitation,  propose a new classification ap- proach combining the AIRS and possibility theory. The new approach is allowing to deal with uncertain attribute values of training instances. The uncertainty is expressed via possibility distributions. Experimenta- tions on real data sets from the U.C.I machine learning repository show good performances of the proposed approach."}]