Incremental learning of concept descriptions: A method and experimental results
Machine intelligence 11
C4.5: programs for machine learning
C4.5: programs for machine learning
Elements of machine learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Incremental Induction of Decision Trees
Machine Learning
Mining Association Rules in Preference-Ordered Data
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Classification Strategies Using Certain and Possible Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Constraint Based Incremental Learning of Classification Rules
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Variable Consistency Model of Dominance-Based Rough Sets Approach
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Data mining tasks and methods: Classification: multicriteria classification
Handbook of data mining and knowledge discovery
Computing Approximations of Dominance-Based Rough Sets by Bit-Vector Encodings
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
An Incremental Rule Induction Algorithm Based on Ordering Relations
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Dominance-Based Rough Sets Using Indexed Blocks as Granules
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Dominance-based rough sets using indexed blocks as granules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Bit-vector representation of dominance-based approximation space
Transactions on rough sets XIII
Dominance-Based Rough Sets Using Indexed Blocks as Granules
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Decision rule length as a basis for evaluation of attribute relevance
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Induction of decision rules within the dominance–based rough set approach to the multicriteria and multiattribute classification is considered. Within this framework, we discuss two algorithms: Glance and an extended version of AllRules. The important characteristics of Glance is that it induces the set of all dominance–based rules in an incremental way. On the other hand, AllRules induces in a non–incremental way the set of all robust rules, i.e. based on objects from the set of learning examples. The main aim of this study is to compare both these algorithms. We experimentally evaluate them on several data sets. The results show that Glance and AllRules are complementary algorithms. The first one works very efficiently on data sets described by a low number of condition attributes and a high number of objects. The other one, conversely, works well on data sets characterized by a high number of attributes and a low number of objects.