Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose
<p dir="ltr">Long term sensors drift is a challenging problem to solve for instruments like an Electronic Nose System (ENS). These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performanc...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2020
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| _version_ | 1864513561245515776 |
|---|---|
| author | Atiq Ur Rehman (8843024) |
| author2 | Samir Brahim Belhaouari (9427347) Muhammad Ijaz (677144) Amine Bermak (1895947) Mounir Hamdi (14150652) |
| author2_role | author author author author |
| author_facet | Atiq Ur Rehman (8843024) Samir Brahim Belhaouari (9427347) Muhammad Ijaz (677144) Amine Bermak (1895947) Mounir Hamdi (14150652) |
| author_role | author |
| dc.creator.none.fl_str_mv | Atiq Ur Rehman (8843024) Samir Brahim Belhaouari (9427347) Muhammad Ijaz (677144) Amine Bermak (1895947) Mounir Hamdi (14150652) |
| dc.date.none.fl_str_mv | 2020-12-02T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/jsen.2020.3041949 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multi-Classifier_Tree_With_Transient_Features_for_Drift_Compensation_in_Electronic_Nose/24006795 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Sensors Transient analysis Sensor systems Sensor phenomena and characterization Gas detectors Steady-state Time factors Artificial olfaction Electronic nose Heuristic optimization Industrial gases Sensors drift |
| dc.title.none.fl_str_mv | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Long term sensors drift is a challenging problem to solve for instruments like an Electronic Nose System (ENS). These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. Moreover, the response of an electronic nose system depends on the variable response of the sensors and a delay is expected in reaching a steady state by the sensors. In this paper, these two problems of `sensors long term drift' and `delayed response' are solved simultaneously to propose a robust and fast electronic nose system, with following merits: (i) only initial transient state features are used in the proposed system without waiting for the sensors to reach a steady state, (ii) a modified boxplot approach is used to handle noisy/drifted data points as a preprocessing step before the classification setup, (iii) a heuristic tree classification approach with optimized transient features is proposed, (iv) the proposed approach only relies on adapted ML methods contrary to the traditional approaches like system recalibration or sensors replacement for handling sensors drift, and (v) the proposed ML model does not require any target domain data and uses only the source domain data for learning the classifier, opposed to the other ML solutions available in the existing literature. The proposed method is tested using a large scale gas sensors drift benchmark dataset available freely on UCI Machine Learning repository and is found better than the existing state-of-the art approaches with an overall accuracy of 87.34%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Sensors Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jsen.2020.3041949" target="_blank">https://dx.doi.org/10.1109/jsen.2020.3041949</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1967bbfbd4238e2e4af4cc0cf9142631 |
| identifier_str_mv | 10.1109/jsen.2020.3041949 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24006795 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic NoseAtiq Ur Rehman (8843024)Samir Brahim Belhaouari (9427347)Muhammad Ijaz (677144)Amine Bermak (1895947)Mounir Hamdi (14150652)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningSensorsTransient analysisSensor systemsSensor phenomena and characterizationGas detectorsSteady-stateTime factorsArtificial olfactionElectronic noseHeuristic optimizationIndustrial gasesSensors drift<p dir="ltr">Long term sensors drift is a challenging problem to solve for instruments like an Electronic Nose System (ENS). These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. Moreover, the response of an electronic nose system depends on the variable response of the sensors and a delay is expected in reaching a steady state by the sensors. In this paper, these two problems of `sensors long term drift' and `delayed response' are solved simultaneously to propose a robust and fast electronic nose system, with following merits: (i) only initial transient state features are used in the proposed system without waiting for the sensors to reach a steady state, (ii) a modified boxplot approach is used to handle noisy/drifted data points as a preprocessing step before the classification setup, (iii) a heuristic tree classification approach with optimized transient features is proposed, (iv) the proposed approach only relies on adapted ML methods contrary to the traditional approaches like system recalibration or sensors replacement for handling sensors drift, and (v) the proposed ML model does not require any target domain data and uses only the source domain data for learning the classifier, opposed to the other ML solutions available in the existing literature. The proposed method is tested using a large scale gas sensors drift benchmark dataset available freely on UCI Machine Learning repository and is found better than the existing state-of-the art approaches with an overall accuracy of 87.34%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Sensors Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jsen.2020.3041949" target="_blank">https://dx.doi.org/10.1109/jsen.2020.3041949</a></p>2020-12-02T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/jsen.2020.3041949https://figshare.com/articles/journal_contribution/Multi-Classifier_Tree_With_Transient_Features_for_Drift_Compensation_in_Electronic_Nose/24006795CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240067952020-12-02T00:00:00Z |
| spellingShingle | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose Atiq Ur Rehman (8843024) Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Sensors Transient analysis Sensor systems Sensor phenomena and characterization Gas detectors Steady-state Time factors Artificial olfaction Electronic nose Heuristic optimization Industrial gases Sensors drift |
| status_str | publishedVersion |
| title | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| title_full | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| title_fullStr | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| title_full_unstemmed | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| title_short | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| title_sort | Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Sensors Transient analysis Sensor systems Sensor phenomena and characterization Gas detectors Steady-state Time factors Artificial olfaction Electronic nose Heuristic optimization Industrial gases Sensors drift |