J
Johannes Fürnkranz
Researcher at Johannes Kepler University of Linz
Publications - 290
Citations - 9241
Johannes Fürnkranz is an academic researcher from Johannes Kepler University of Linz. The author has contributed to research in topics: Pairwise comparison & Heuristics. The author has an hindex of 43, co-authored 279 publications receiving 8340 citations. Previous affiliations of Johannes Fürnkranz include University of Vienna & Austrian Research Institute for Artificial Intelligence.
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Journal ArticleDOI
Multilabel classification via calibrated label ranking
TL;DR: 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.
Journal ArticleDOI
Separate-and-Conquer Rule Learning
TL;DR: This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy and analyzes them along three different dimensions, namely their search, language and overfitting avoidance biases.
Journal ArticleDOI
Label ranking by learning pairwise preferences
TL;DR: 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.
Journal ArticleDOI
Round robin classification
TL;DR: An empirical evaluation of round robin classification, implemented as a wrapper around the Ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository shows that the technique is very likely to improve Ripper's classification accuracy without having a high risk of decreasing it.
Book ChapterDOI
Incremental reduced error pruning
TL;DR: Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy, however, the experiments show that the use of the algorithm cannot be recommended for domains which require a very specific concept description.