Scalable multilabel learning based on feature and label dimensionality reduction
type: Item
instance of: scholarly article
title: Scalable multilabel learning based on feature and label dimensionality reduction
zbMATH Open document ID: 1407.68401
DOI: 10.1155/2018/6292143
author: Jaesung Lee
author: Dae-Won Kim
published in: Complexity
publication date: 2018-12-17T00:00:00Z
review text: Summary: The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increased computational burden owing to the concurrent learning of multiple labels. A straightforward approach to address this issue is feature selection; however, it may be insufficient to satisfy the practical constraints because the computational cost for feature selection can be impractical when the number of labels is large. In this study, we propose an efficient multilabel feature selection method to achieve scalable multilabel learning when the number of labels is large. The empirical experiments on several multilabel datasets show that the multilabel learning process can be boosted without deteriorating the discriminating power of the multilabel classifier.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 93A14
Mathematics Subject Classification ID: 94A17
zbMATH DE Number: 6994163
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/6292143
OpenAlex ID: W2893237591
cites work: Fast multi-label feature selection based on information-theoretic feature ranking
cites work: Theoretical and empirical analysis of ReliefF and RReliefF
cites work: Feature selection for multi-label naive Bayes classification
cites work: Designing multi-label classifiers that maximize \(F\) measures: state of the art
cites work: Q4023085
cites work: Multilabel classification with meta-level features in a learning-to-rank framework
cites work: Q3093335
cites work: Multiple Comparisons Among Means
Wikidata QID: Q129202205
description: scientific article; zbMATH DE number 6994163