Greedy active learning algorithm for logistic regression models

We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy v...

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Veröffentlicht in:Computational statistics & data analysis Jg. 129; S. 119 - 134
Hauptverfasser: Hsu, Hsiang-Ling, Chang, Yuan-chin Ivan, Chen, Ray-Bing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.01.2019
Schlagworte:
[formula omitted]-efficiency criterion
Active learning algorithm
algorithms
data collection
experimental design
Forward selection
Graft optimization
regression analysis
telescopes
D-efficiency criterion
Active learning algorithm
Forward selection
Graft optimization
ISSN:0167-9473, 1872-7352
Online-Zugang:Volltext
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Zusammenfassung:We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model compared with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) and a MAGIC gamma telescope data set to confirm the performance of our method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2018.08.013