research-article

Room Occupancy Detection using Modified Stacking

Authors:

Pawalai Kraipeerapun,

Somkid AmornsamankulAuthors Info & Claims

ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

Pages 162 - 166

https://doi.org/10.1145/3055635.3056597

Published: 24 February 2017 Publication History

Abstract

Occupancy detection is a binary classification task. However, in this paper, stacking for multiclass classification is applied to detect occupancy of a room. Neural network with duo outputs are combined with stacking. The outputs of stacking for multiclass classification are then integrated to get a binary classification. The occupancy detection dataset obtained from UCI Machine Learning Repository is used in the experiment. It is found that our proposed stacking technique provides better accuracy result than the traditional stacking for binary classification.

References

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Kraipeerapun, P., Amornsamankul, S., Fung, C.C., and Nakkrasae S. 2009. Applying Duo Output Neural Networks to Solve Single Output Regression Problem. In Proceedings of The 16th International Conference on Neural Information Processing (Bangkok, Thailand, December 01-05, 2009). ICONIP '09. 554--561.

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Kraipeerapun, P. and Amornsamankul, S. 2016. Using Falsity Data in the Stacking Technique. In Proceedings of the International Conference on Computational Intelligence and Applications (Jeju Island, Korea, August 27-29, 2016). ICCIA'16. 1--5.

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Cited By

Sourav MYavari EGao XMaskrey JZheng YLubecke VBoric-Lubecke O(2024)Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy ConsiderationSensors10.3390/s2412373924:12(3739)Online publication date: 8-Jun-2024
https://doi.org/10.3390/s24123739
Khan IZedadra OGuerrieri ASpezzano G(2024)Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future DirectionsSensors10.3390/s2411327624:11(3276)Online publication date: 21-May-2024
https://doi.org/10.3390/s24113276
Fizza GKadir KNasir HIdrus SMohamed M(2024)Classifying Occupancy Levels in Smart Building by Experimental Evaluation of KNN and its Variants2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC60896.2024.10560807(1-6)Online publication date: 20-May-2024
https://doi.org/10.1109/I2MTC60896.2024.10560807
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ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

February 2017

545 pages

ISBN:9781450348171

DOI:10.1145/3055635

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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Published: 24 February 2017

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ICMLC 2017

ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing

February 24 - 26, 2017

Singapore, Singapore

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Cited By

Sourav MYavari EGao XMaskrey JZheng YLubecke VBoric-Lubecke O(2024)Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy ConsiderationSensors10.3390/s2412373924:12(3739)Online publication date: 8-Jun-2024
https://doi.org/10.3390/s24123739
Khan IZedadra OGuerrieri ASpezzano G(2024)Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future DirectionsSensors10.3390/s2411327624:11(3276)Online publication date: 21-May-2024
https://doi.org/10.3390/s24113276
Fizza GKadir KNasir HIdrus SMohamed M(2024)Classifying Occupancy Levels in Smart Building by Experimental Evaluation of KNN and its Variants2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC60896.2024.10560807(1-6)Online publication date: 20-May-2024
https://doi.org/10.1109/I2MTC60896.2024.10560807
Weber MBanihashemi FMandl PJacobsen HMayer R(2023)Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy DetectionProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623718(1-10)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623718
Chitnis SSomu NKowli A(2023)Occupancy estimation with environmental sensors: The possibilities and limitationsEnergy and Built Environment10.1016/j.enbenv.2023.09.003Online publication date: Sep-2023
https://doi.org/10.1016/j.enbenv.2023.09.003
Fayed NElmogy MAtwan AEl-Daydamony E(2022)Efficient Occupancy Detection System Based on Neutrosophic Weighted Sensors Data FusionIEEE Access10.1109/ACCESS.2022.314634610(13400-13427)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3146346
Varnosfaderani MHeydarian AJazizadeh F(2022) Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO 2 , and Other Environmental Factors Computing in Civil Engineering 202110.1061/9780784483893.087(705-712)Online publication date: 24-May-2022
https://doi.org/10.1061/9780784483893.087
Hasan ZRoy N(2021) Trending machine learning models in cyber‐physical building environment: A survey WIREs Data Mining and Knowledge Discovery10.1002/widm.142211:5Online publication date: 29-Jun-2021
https://doi.org/10.1002/widm.1422
Hao JYuan XYang YWang RZhuang YLuo J(2018)Visible Light Based Occupancy Inference Using Ensemble LearningIEEE Access10.1109/ACCESS.2018.28096126(16377-16385)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2809612

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