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Multilabel classification via calibrated label ranking
TLDR
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. Expand
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
TLDR
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. Expand
FURIA: an algorithm for unordered fuzzy rule induction
TLDR
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. Expand
Label ranking by learning pairwise preferences
TLDR
This work shows that a simple (weighted) voting strategy minimizes risk with respect to the well-known Spearman rank correlation and compares RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Expand
On label dependence and loss minimization in multi-label classification
TLDR
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. Expand
Learning from ambiguously labeled examples
TLDR
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. Expand
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification
TLDR
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. Expand
An Approach to Modelling and Simulation of Uncertain Dynamical Systems
  • E. Hüllermeier
  • Mathematics, Computer Science
  • Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 1 April 1997
TLDR
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. Expand
Decision tree and instance-based learning for label ranking
TLDR
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. Expand
Combining instance-based learning and logistic regression for multilabel classification
TLDR
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. Expand
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