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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
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
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
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
Label Ranking Methods based on the Plackett-Luce Model
TLDR
This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model, which estimates a global model in which the PL parameters are represented as functions of the instance. Expand
An Exact Algorithm for F-Measure Maximization
TLDR
This paper presents an algorithm which is not only computationally efficient but also exact, regardless of the underlying distribution, and illustrates its practical performance by means of experimental results for multi-label classification. Expand
Top-k Selection based on Adaptive Sampling of Noisy Preferences
TLDR
This work proposes and formally analyze a general preference-based racing algorithm that is instantiate with three specific ranking procedures and corresponding sampling schemes, and assumes that alternatives can be compared in terms of pairwise preferences. Expand
Predicting Partial Orders: Ranking with Abstention
TLDR
A general approach to ranking with partial abstention is proposed as well as evaluation metrics for measuring the correctness and completeness of predictions, able to achieve a reasonable trade-off between these two criteria. Expand
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