This paper is concerned with a general framework for designing a fuzzy rule-based classifier. Str... more This paper is concerned with a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. The classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy.
Informatica (lithuanian Academy of Sciences), 2008
This paper is concerned with the problem of image analysis based detection of local defects embed... more This paper is concerned with the problem of image analysis based detection of local defects embedded in particleboard surfaces. Though simple, but efficient technique developed is based on the analysis of the discrete probability distribution of the image intensity values and the 2D discrete Walsh transform. Robust global features characterizing a surface texture are extracted and then analyzed by a pattern classifier. The classifier not only assigns the pattern into the quality or detective class, but also provides the certainty value attributed to the decision. A 100% correct classification accuracy was obtained when testing the technique proposed on a set of 200 images.
This paper presents a general framework for designing a fuzzy rule-based classifier. Structure an... more This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, the classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy. By contrast, the classification accuracy of unseen data is increased due to the elimination.
Informatica (lithuanian Academy of Sciences), 2008
Non-invasive physiological monitors are important subsystems of intensive care informatic systems... more Non-invasive physiological monitors are important subsystems of intensive care informatic systems. New innovative information methods and technology are presented for non-invasive human brain volumetric pulse wave physiological monitoring.
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Papers by jonas guzaitis