Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Dembczynski, Krzysztof"'
Downsampling or under-sampling is a technique that is utilized in the context of large and highly imbalanced classification models. We study optimal downsampling for imbalanced classification using generalized linear models (GLMs). We propose a pseud
Externí odkaz:
http://arxiv.org/abs/2410.08994
We consider sequential maximization of performance metrics that are general functions of a confusion matrix of a classifier (such as precision, F-measure, or G-mean). Such metrics are, in general, non-decomposable over individual instances, making th
Externí odkaz:
http://arxiv.org/abs/2406.14743
Autor:
Schultheis, Erik, Kotłowski, Wojciech, Wydmuch, Marek, Babbar, Rohit, Borman, Strom, Dembczyński, Krzysztof
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework. We mainly focus on metrics linearly decomposable into a sum of binary classification utilities applied separately to eac
Externí odkaz:
http://arxiv.org/abs/2401.16594
Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant labels from a very large set of possible labels. As such, it is characterized by long-tail labels, i.e., most labels have very few positive instances. With
Externí odkaz:
http://arxiv.org/abs/2311.05081
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its
Externí odkaz:
http://arxiv.org/abs/2207.13186
Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hierarchical
Externí odkaz:
http://arxiv.org/abs/2203.06676
Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels. Recently, XMLC has been widely applied to diverse web applications such
Externí odkaz:
http://arxiv.org/abs/2110.10803
Autor:
Jasinska-Kobus, Kalina, Wydmuch, Marek, Dembczynski, Krzysztof, Kuznetsov, Mikhail, Busa-Fekete, Robert
Extreme multi-label classification (XMLC) is a learning task of tagging instances with a small subset of relevant labels chosen from an extremely large pool of possible labels. Problems of this scale can be efficiently handled by organizing labels as
Externí odkaz:
http://arxiv.org/abs/2009.11218
Autor:
Jasinska-Kobus, Kalina, Wydmuch, Marek, Thiruvenkatachari, Devanathan, Dembczyński, Krzysztof
We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by
Externí odkaz:
http://arxiv.org/abs/2007.04451
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctn
Externí odkaz:
http://arxiv.org/abs/1906.08129