Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems
المؤلفون المشاركون
Luo, Guangchun
Niu, Weina
Ma, Zhiyuan
Wang, Nan
Qin, Ke
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-02-12
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems.
However, labeled data are rare in practice, which makes supervised learning methods hard to be applied.
Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios.
In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM_T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning.
Furthermore, we converted DAELM_T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand.
Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy.
As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ma, Zhiyuan& Luo, Guangchun& Qin, Ke& Wang, Nan& Niu, Weina. 2018. Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1215829
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ma, Zhiyuan…[et al.]. Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-17.
https://search.emarefa.net/detail/BIM-1215829
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ma, Zhiyuan& Luo, Guangchun& Qin, Ke& Wang, Nan& Niu, Weina. Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1215829
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1215829
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر