دانلود مقاله ISI انگلیسی شماره 42614
عنوان فارسی مقاله

کشف فعالیت و مدل سازی با داده های برچسب و بدون برچسب در محیط های هوشمند

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
42614 2015 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Activity discovering and modelling with labelled and unlabelled data in smart environments
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 14, 15 August 2015, Pages 5800–5810

کلمات کلیدی
داده کاوی - فراگیری ماشین - به رسمیت شناختن فعالیت - اندازه گیری شباهت - داده های برچسب و بدون برچسب - محیط هوشمند
پیش نمایش مقاله
پیش نمایش مقاله کشف فعالیت و مدل سازی با داده های برچسب و بدون برچسب در محیط های هوشمند

چکیده انگلیسی

In the past decades, activity recognition had aroused great interest for the community of context-awareness computing and human behaviours monitoring. However, most of the previous works focus on supervised methods in which the data labelling is known to be time-consuming and sometimes error-prone. In addition, due to the randomness and erratic nature of human behaviours in realistic environments, supervised models trained with data from certain subject might not be scaled to others. Further more, unsupervised methods, with little knowledge about the activities to be recognised, might result in poor performance and high clustering overhead. To this end, we propose an activity recognition model with labelled and unlabelled data in smart environments. With small amount of labelled data, we discover activity patterns from unlabelled data based on proposed similarity measurement algorithm. Our system does not require large amount of data to be labelled while the proposed similarity measurement method is effective to discover length-varying, disordered and discontinuous activity patterns in smart environments. Therefore, our methods yield comparable performance with much less labelled data when compared with traditional supervised activity recognition, and achieve higher accuracy with lower clustering overhead compared with unsupervised methods. The experiments based on real datasets from the smart environments demonstrate the effectiveness of our method, being able to discover more than 90% of original activities from the unlabelled data, and the comparative experiments show that our methods are capable of providing a better trade-off, regarding the accuracy, overhead and labelling efforts, between the supervised and unsupervised methods.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.