کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6861926 | 1439260 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Selective Multiple Instance Transfer Learning Method for Text Categorization Problems
ترجمه فارسی عنوان
یک روش انتخاب فراگیر نمونه چندگانه برای مشکلات طبقه بندی متن
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کلمات کلیدی
داده کاوی، انتقال یادگیری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn a distinctive classifier from bags of instances. This paper addresses the problem of the transfer learning-based multiple instance method for text categorization problem. To provide a safe transfer of knowledge from a source task to a target task, this paper proposes a new approach, called selective multiple instance transfer learning (SMITL), which selects the case that the multiple instance transfer learning will work in step one, and then builds a multiple instance transfer learning classifier in step two. Specifically, in the first step, we measure whether the source task and the target task are related or not by investigating the similarity of the positive features of both tasks. In the second step, we construct a transfer learning-based multiple instance method to transfer knowledge from a source task to a target task if both tasks are found to be related in the first step. Our proposed approach explicitly addresses the problem of safe transfer of knowledge for multiple instance learning on the text classification problem. Extensive experiments have shown that SMITL can determine whether the two tasks are related for most data sets, and outperforms classic multiple instance learning methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 141, 1 February 2018, Pages 178-187
Journal: Knowledge-Based Systems - Volume 141, 1 February 2018, Pages 178-187
نویسندگان
Bo Liu, Yanshan Xiao, Zhifeng Hao,