Autoencoder-based One-class Classification Technique for Event Prediction

Seung Yeop Shin, Han joon Kim

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

3 Scopus citations

Abstract

This paper proposes an autoencoder-based one-class classification technique to predict a specific event such as the occurrence of a fire in a specific building. Basically, a binary classification system that uses machine learning to identify fire-risk buildings requires ‘positive’ fire data and ‘negative’ non-fire data. However, the fire-risk building data that can be actually obtained have a single class data that includes only the data of the occurrence of the fire and does not include the data of the ‘non-occurrence’. In this situation, PU (Positive-Unlabeled) learning which uses ‘unlabeled’ data can be an effective way of generating the fire prediction model. The autoencoder generates new features from the unlabeled data, with which a predictive model for predicting the fire-risk buildings is built through PU learning.

Original languageEnglish
Title of host publicationCCIOT 2019 - 2019 4th International Conference on Cloud Computing and Internet of Things
PublisherAssociation for Computing Machinery
Pages54-58
Number of pages5
ISBN (Electronic)9781450372411
DOIs
StatePublished - 20 Sep 2019
Event4th International Conference on Cloud Computing and Internet of Things, CCIOT 2019 - Tokyo, Japan
Duration: 20 Sep 201922 Sep 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Conference on Cloud Computing and Internet of Things, CCIOT 2019
Country/TerritoryJapan
CityTokyo
Period20/09/1922/09/19

Keywords

  • Autoencoder
  • Deep Learning
  • Feature Extraction
  • Machine Learning
  • One-class Classification

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