Optimization and interpretation of rule-based classifiers

Machine learning methods are frequently used to create rule-based classifiers. For continuous features linguistic variables used in conditions of the rules are defined by membership functions. These linguistic variables should be optimized at the level of single rules or sets of rules. Assuming the Gaussian uncertainty of input values allows to increase the accuracy of predictions and to estimate probabilities of different classes. Detailed interpretation of relevant rules is possible using (probabilistic) confidence intervals. A real life example of such interpretation is given for personality disorders. The approach to optimization and interpretation described here is applicable to any rule-based system.