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Multilabel classification via calibrated label ranking
- Johannes Fürnkranz, E. Hüllermeier, E. Mencía, K. Brinker
- Computer ScienceMachine Learning
- 1 November 2008
This work proposes a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of existing approaches and suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique.
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
This paper formalize and analyze MLC within a probabilistic setting, and proposes a new method for MLC that generalizes and outperforms another approach, called classifier chains, that was recently introduced in the literature.
FURIA: an algorithm for unordered fuzzy rule induction
A novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm, which significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.
Label ranking by learning pairwise preferences
Learning from ambiguously labeled examples
This paper is concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example, and shows that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples.
On label dependence and loss minimization in multi-label classification
- K. Dembczynski, W. Waegeman, Weiwei Cheng, E. Hüllermeier
- Computer ScienceMachine Learning
- 1 July 2012
It is claimed that two types of label dependence should be distinguished, namely conditional and marginal dependence, and three scenarios in which the exploitation of one of these types of dependence may boost the predictive performance of a classifier are presented.
Combining instance-based learning and logistic regression for multilabel classification
This paper proposes a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases, and allows one to capture interdependencies between labels and to combine model-based and similarity-based inference for multILabel classification.
An Approach to Modelling and Simulation of Uncertain Dynamical Systems
- E. Hüllermeier
- Computer ScienceInt. J. Uncertain. Fuzziness Knowl. Based Syst.
- 1 April 1997
It will be shown that all (reasonable) fuzzy functions can be approximated to any degree of accuracy in this way and an interpretation of fuzzy initial value problems is proposed.
Decision tree and instance-based learning for label ranking
New methods for label ranking are introduced that complement and improve upon existing approaches and are extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction.
Pairwise Preference Learning and Ranking
The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example.