Parallel interacting multiview learning: An application to prediction of protein sub-nuclear location

C. Okan Sakar, Olcay Kursun, Huseyin Seker, Fikret Gürgen, Nizamettin Aydin, Oleg V. Favorov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In some machine learning problems, the dataset has multiple views which may be obtained using different sensors or applying different sampling techniques. These views may have sufficient or partial information about the target concept. In this paper, a method that we called parallel interacting multiview learning (PIML) is proposed in which the views interact during the training process using the predictions of each other together with their original features. This way, the views are expected to strengthen the prediction accuracies of the other views feeding their predictions to the others even during the training process. This technique avoids the way of simply merging features of all views and reaches higher accuracy than its counterparts that do not interact during learning but only combine their predictions after the learning process. PIML is demonstrated on a real bioinformatics dataset for predicting protein sub-nuclear locations.

Original languageEnglish
Title of host publicationFinal Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 - Larnaca, Cyprus
Duration: 4 Nov 20097 Nov 2009

Publication series

NameFinal Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009

Conference

Conference9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009
Country/TerritoryCyprus
CityLarnaca
Period4/11/097/11/09

Keywords

  • Curse of dimensionality
  • Ensemble methods
  • Multiview learning
  • Pattern recognition
  • Protein structural classes

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