C4.5: programs for machine learning
C4.5: programs for machine learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Generalized Patterns
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Approximate Reducts and Association Rules - Correspondence and Complexity Results
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
ELEM2: A Learning System for More Accurate Classifications
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support
Engineering Applications of Artificial Intelligence
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
On algorithm for building of optimal α-decision trees
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Relationships between depth and number of misclassifications for decision trees
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Online learning algorithm for ensemble of decision rules
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Rule-based estimation of attribute relevance
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
A hierarchical approach to multimodal classification
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Ensembles of Classifiers Based on Approximate Reducts
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
On Algorithm for Constructing of Decision Trees with Minimal Depth
Fundamenta Informaticae
Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis
Fundamenta Informaticae
Dynamic programming approach to optimization of approximate decision rules
Information Sciences: an International Journal
KES'12 Proceedings of the 16th international conference on Knowledge Engineering, Machine Learning and Lattice Computing with Applications
Relationships Between Length and Coverage of Decision Rules
Fundamenta Informaticae - Dedicated to the Memory of Professor Manfred Kudlek
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This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δγ(T) which nodes are subtables of the decision table T given by systems of equations of the kind “attribute = value”. This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δγ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.