Restricted Boltzmann machines for unsupervised feature selection with partial least square feature extractor for microarray datasets

Lintang Adyuta Sutawika, Ito Wasito

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

3 Citations (Scopus)

Abstract

Feature selection is a key component in microarray data analysis. This is due to the fact that microarray datasets consists of features that are far exceed the number of instances. High dimensional data are also known to contain significant amount of noise and irrelevant variables that do not contribute to classification tasks and may even hinder classification performance. In this paper, a feature selection method which consists of two stages is proposed. At the first step, feature selection is done through a stacked Restricted Boltzmann Machines by means of comparing the error between reconstructed data and the original data. The next stage will use Partial Least Square to extract synthesis features from the previously selected features that will be then used for classification. The performance of the proposed method is done through the classification of ten microarray datasets that are widely used. The proposed model is able to out perform state-of-the-art in 2 datasets, namely 82.11% for GLIOMA and 72.39% for Breast datasets.

Original languageEnglish
Title of host publication2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages257-260
Number of pages4
ISBN (Electronic)9781538631720
DOIs
Publication statusPublished - 4 May 2018
Event9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 - Jakarta, Indonesia
Duration: 28 Oct 201729 Oct 2017

Publication series

Name2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Volume2018-January

Conference

Conference9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
CountryIndonesia
CityJakarta
Period28/10/1729/10/17

Keywords

  • deep learning
  • gene expression
  • microarray data analysis
  • restricted boltzmann machine

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