On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Artificial Intelligence Review - Special issue on lazy learning
Advances in instance-based learning algorithms
Advances in instance-based learning algorithms
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Decision trees for ordinal classification
Intelligent Data Analysis
Experience-Based Decision Making: A Satisficing Decision Tree Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On the Use of Clustering in Possibilistic Decision Tree Induction
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A hierarchical conflict resolution method for multi-agent path planning
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
SIM-PDT: a similarity based possibilistic decision tree approach
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
The Journal of Machine Learning Research
Rank-loss support instance machines for MIML instance annotation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
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Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting (nearest neighbor classification, decision tree learning, and rule induction) and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice.