Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Multi-Instance Learning Based Web Mining
Applied Intelligence
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
CASON '10 Proceedings of the 2010 International Conference on Computational Aspects of Social Networks
Construction of α-decision trees for tables with many-valued decisions
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Decision rules for decision tables with many-valued decisions
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Multi-instance multi-label learning
Artificial Intelligence
Decision trees for hierarchical multilabel classification: a case study in functional genomics
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Extracting emotions from music data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Combinatorial Machine Learning: A Rough Set Approach
Combinatorial Machine Learning: A Rough Set Approach
Fundamenta Informaticae - Concurrency Specification and Programming CS&P
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In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.