@inproceedings{1b59b3080f20491cbfc2576747e06039,
title = "Autoencoder-based One-class Classification Technique for Event Prediction",
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 {\textquoteleft}positive{\textquoteright} fire data and {\textquoteleft}negative{\textquoteright} 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 {\textquoteleft}non-occurrence{\textquoteright}. In this situation, PU (Positive-Unlabeled) learning which uses {\textquoteleft}unlabeled{\textquoteright} 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.",
keywords = "Autoencoder, Deep Learning, Feature Extraction, Machine Learning, One-class Classification",
author = "Shin, {Seung Yeop} and Kim, {Han joon}",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 4th International Conference on Cloud Computing and Internet of Things, CCIOT 2019 ; Conference date: 20-09-2019 Through 22-09-2019",
year = "2019",
month = sep,
day = "20",
doi = "10.1145/3361821.3361831",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "54--58",
booktitle = "CCIOT 2019 - 2019 4th International Conference on Cloud Computing and Internet of Things",
}