کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947805 | 1439597 | 2017 | 27 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Object-to-group probabilistic distance measure for uncertain data classification
ترجمه فارسی عنوان
اندازه گیری فاصله از نظر شیء به گروه برای طبقه بندی نامشخص
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کلمات کلیدی
داده کاوی، داده های نامعلوم، طبقه بندی، اقدامات احتمالی فاصله،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Uncertain objects, where each feature is represented by multiple observations or a given or fitted probability density function, arise in applications such as sensor networks, moving object databases and medical and biological databases. We propose a methodology to classify uncertain objects based on a new probabilistic distance measure between an uncertain object and a group of uncertain objects. This object-to-group probabilistic distance measure is unique in that it accounts separately for the correlations among the features within each class and within each object. We compare the proposed object-to-group classifier to two existing classifiers, namely, the K-Nearest Neighbor classifier on object means (certain-KNN) and the uncertain-naïve Bayes classifier. In addition, we compare the object-to-group classifier to an uncertain K-Nearest Neighbor classifier (uncertain-KNN), also proposed here, that uses existing probabilistic distance measures for object-to-object distances. We illustrate the advantages of the proposed classifiers with both simulated and real data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 230, 22 March 2017, Pages 143-151
Journal: Neurocomputing - Volume 230, 22 March 2017, Pages 143-151
نویسندگان
Behnam Tavakkol, Myong Kee Jeong, Susan L. Albin,