Intelligent data analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. This paper will briefly look at some of the key issues in intelligent data analysis, discuss the opportunities f o r soft computing in this context, and present .several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consistency checking between mass spectral data with proposed chemical structures, screening f o r glaucoma and other eye diseases, forecasting of visual field deterioration, and diagnosis in an oil refinery involving multivariate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies.
Case studies are all related with forecasting for visual field deterioration, real world problem solving, screening for eye diseases, consistency checking for chemical structures REF .
Soft computing for intelligent data analysis
18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)
18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)
Computer Science