Efficient model selection for regularized classification by exploiting unlabeled data

Georgios Balikas, Ioannis Partalas, Eric Gaussier, Rohit Babbar, Massih Reza Amini

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)


Hyper-parameter tuning is a resource-intensive task when optimizing classification models. The commonly used k-fold cross validation can become intractable in large scale settings when a classifier has to learn billions of parameters. At the same time, in real-world, one often encounters multi-class classification scenarios with only a few labeled examples; model selection approaches often offer little improvement in such cases and the default values of learners are used.We propose bounds for classification on accuracy and macro measures (precision, recall, F1) that motivate efficient schemes for model selection and can benefit from the existence of unlabeled data. We demonstrate the advantages of those schemes by comparing them with k-fold cross validation and hold-out estimation in the setting of large scale classification.

Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, Proceedings
EditorsTijl De Bie, Matthijs van Leeuwen, Elisa Fromont
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)9783319244655
ISBN (Print)9783319244648
Publication statusPublished - 22 Nov 2015
Event14th International Symposium on Intelligent Data Analysis, IDA 2015 - Saint Etienne, France
Duration: 22 Oct 201524 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Symposium on Intelligent Data Analysis, IDA 2015
CitySaint Etienne

Bibliographical note

Funding Information:
This work is partially supported by the CIFRE N 28/2015 and by the LabEx PERSYVAL Lab ANR-11-LABX-0025.

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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