RBM-SMOTE: Restricted boltzmann machines for synthetic minority oversampling technique

Maciej Zięba*, Jakub M. Tomczak, Adam Gonczarek

*Corresponding author for this work

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

Abstract

The problem of imbalanced data, i.e., when the class labels are unequally distributed, is encountered in many real-life application, e.g., credit scoring, medical diagnostics. Various approaches aimed at dealing with the imbalanced data have been proposed. One of the most well known data pre-processing method is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE may generate examples which are artificial in the sense that they are impossible to be drawn from the true distribution. Therefore, in this paper, we propose to apply Restricted Boltzmann Machine to learn an intermediate representation which transform the SMOTE examples to the ones approximately drawn from the true distribution. At the end of the paper we perform an experiment using credit scoring dataset.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 7th Asian Conference, ACIIDS 2015, Proceedings
EditorsNgoc Thanh Nguyen, Raymond Kosala, Ngoc Thanh Nguyen, Bogdan Trawiński
PublisherSpringer Verlag
Pages377-386
Number of pages10
ISBN (Electronic)9783319157016
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event7th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2015 - Bali, Indonesia
Duration: 23 Mar 201525 Mar 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9011
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2015
CountryIndonesia
CityBali
Period23/03/1525/03/15

Keywords

  • Imbalanced data
  • Oversampling
  • RBM
  • SMOTE

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