کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946172 | 1439281 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A multiple-instance stream learning framework for adaptive document categorization
ترجمه فارسی عنوان
یک چارچوب یادگیری جریان چندگانه برای طبقه بندی سازگار با سند
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کلمات کلیدی
جریان داده ها، یادگیری چند نمونه ای،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
The task of document categorization is to classify documents from a stream as relevant or non-relevant to a particular user interest so as to reduce information overload. Existing solutions typically perform classification at the document level, i.e., a document is returned as relevant if at least a part of the document is of interest of the user. In this paper, we propose a novel multiple-instance stream learning framework for adaptive document categorization, named MIS-DC. Our proposed approach has the ability of making accuracy prediction at both the document level and the block level, while only requires labeling the training documents at the document level. In addition, our proposed approach can also provide adaptive document categorization by detecting and handling concept drift at a finer granularity when data streams evolve over time, thereby yielding higher prediction accuracy than existing data stream algorithms. Experiments on benchmark and real-world datasets have demonstrated the effectiveness of our proposed approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 120, 15 March 2017, Pages 198-210
Journal: Knowledge-Based Systems - Volume 120, 15 March 2017, Pages 198-210
نویسندگان
Yanshan Xiao, Bo Liu, Jie Yin, Zhifeng Hao,