How to process uncertainty in machine learning?

Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate the merits and problems when dealing with uncertainty in machine learning and to give an overview about methodologies which fall under the framework of neurofuzzy methods, in particular fuzzy-clustering on the one side and fuzzy inference systems on the other side. 1 Areas dealing with uncertainty Uncertainty and fuzziness are popular phenomena in many application areas such as medicine (medical diagnosis is often not crisp but there exist various degrees of illness e.g. for psychical diseases such as phobia), image processing (areas at object borders or at overlapping regions can seldom uniquely be classified), linguistics (terms such as ‘high’ or ‘small’ are context dependent), etc. Therefore, uncertainty almost automatically occurs in any application of machine learning. Different types of uncert...

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Barbara Hammer, Thomas Villmann
• Keywords: ESANN 2007, Fuzzy Inference Systems, Machine Learning, Machine Learning Method, Neural Networks