C4.5: programs for machine learning
C4.5: programs for machine learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Machine Learning
Machine Learning
Extraction of informative genes from microarray data
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Journal of Biomedical Informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Information Sciences: an International Journal
Computer Methods and Programs in Biomedicine
Feature selection and classification model construction on type 2 diabetic patients' data
Artificial Intelligence in Medicine
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Artificial Intelligence in Medicine
A parametric methodology for text classification
Journal of Information Science
The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo
Computer Methods and Programs in Biomedicine
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We have been interested in developing an otoneurological decision support system that supports diagnostics of vertigo diseases. In this study, we concentrate on testing its inference mechanism and knowledge discovery method. Knowledge is presented as patterns of classes. Each pattern includes attributes with weight and fitness values concerning the class. With the knowledge discovery method it is possible to form fitness values from data. Knowledge formation is based on frequency distributions of attributes. Knowledge formed by the knowledge discovery method is tested with two vertigo data sets and compared to experts' knowledge. The experts' and machine learnt knowledge are also combined in various ways in order to examine effects of weights on classification accuracy. The classification accuracy of knowledge discovery method is compared to 1- and 5-nearest neighbour method and Naive-Bayes classifier. The results showed that knowledge bases combining machine learnt knowledge with the experts' knowledge yielded the best classification accuracies. Further, attribute weighting had an important effect on the classification capability of the system. When considering different diseases in the used data sets, the performance of the knowledge discovery method and the inference method is comparable to other methods employed in this study.