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Erschienen in:
Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Dynamic Classifier Chain with Random Decision Trees

verfasst von : Moritz Kulessa, Eneldo Loza Mencía

Erschienen in: Discovery Science

Verlag: Springer International Publishing

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Abstract

Classifiers chains (CC) is an effective approach in order to exploit label dependencies in multi-label data. However, it has the disadvantages that the chain is chosen at total random or relies on a pre-specified ordering of the labels which is expensive to compute. Moreover, the same ordering is used for every test instance, ignoring the fact that different orderings might be best suited for different test instances. We propose a new approach based on random decision trees (RDT) which can choose the label ordering for each prediction dynamically depending on the respective test instance. RDT are not adapted to a specific learning task, but in contrast allow to define a prediction objective on the fly during test time, thus offering a perfect test bed for directly comparing different prediction schemes. Indeed, we show that dynamically selecting the next label improves over using a static ordering of the labels under an otherwise unchanged RDT model and experimental environment.

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Vorheriges Kapitel Barricaded Boundary Minority Oversampling LS-SVM for a Biased Binary Classification
Nächstes Kapitel Feature Ranking with Relief for Multi-label Classification: Does Distance Matter?
Fußnoten
1
We assume, w.l.o.g., that \(y_1, y_2, \ldots \) is the ordering of the predicted labels.
 
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Metadaten
Titel
Dynamic Classifier Chain with Random Decision Trees
verfasst von
Moritz Kulessa
Eneldo Loza Mencía
Copyright-Jahr
2018
Buch
Discovery Science

Print ISBN: 978-3-030-01770-5

Electronic ISBN: 978-3-030-01771-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01771-2_3