Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Predictive data mining: a practical guide
Predictive data mining: a practical guide
From optimal hyperplanes to optimal decision trees
Fundamenta Informaticae
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Machine Learning
Generation of Rules from Incomplete Information Systems
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Searching for Features Defined by Hyperplanes
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
A New Rough Set Approach to Multicriteria and Multiattribute Classification
RSCTC '98 Proceedings of the First 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
On growing better decision trees from data
On growing better decision trees from data
Fast rule extraction with binary-coded relations
Intelligent Data Analysis
CHIRA---Convex Hull Based Iterative Algorithm of Rules Aggregation
Fundamenta Informaticae
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In this paper we consider multiple criteria decision aid systems based on decision rules generated from examples. A common problem in such systems is the over-abundance of decision rules, as in many situations the rule generation algorithms produce very large sets of rules. This prolific representation of knowledge provides a great deal of detailed information about the described objects, but is appropriately difficult to interpret and use. One way of solving this problem is to aggregate the created rules into more general ones, e.g. by forming rules of enriched syntax. The paper presents a generalization of elementary rule conditions into linear combinations. This corresponds to partitioning the preference-ordered condition space of criteria with non-orthogonal hyperplanes. The objective of this paper is to introduce the generalized rules into the multiple criteria classification problems and to demonstrate that these problems can be successfully solved using the introduced rules. The usefulness of the introduced solution is finally demonstrated in computational experiments with real-life data sets.