Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Requirements for Successful Verification in Practice
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Iterative feature construction for improving inductive learning algorithms
Expert Systems with Applications: An International Journal
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Non-redundant sequential rules-Theory and algorithm
Information Systems
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Belief networks in classification of laryngopathies based on speech spectrum analysis
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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Our research is devoted to develop a new method of generation of a set of decision rules. This method is compiled using two different mechanisms. The first one is based on applying a new constructive induction algorithm to the investigated dataset. The belief networks are used in this algorithm. The aim is to find the most important descriptive attribute that is calculated on the basis of other attributes. The second part of the presented method constitutes the improvement algorithm that is used in an optimization process of a gathered rule set. The results of our research contain the comparison of classification efficiency using several datasets.