کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10325408 | 671527 | 2005 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Entity identification for heterogeneous database integration-a multiple classifier system approach and empirical evaluation
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Entity identification, i.e., detecting semantically corresponding records from heterogeneous data sources, is a critical step in integrating the data sources. The objective of this research is to develop and evaluate a novel multiple classifier system approach that improves entity identification accuracy. We apply various classification techniques drawn from statistical pattern recognition, machine learning, and artificial neural networks to determine whether two records from different data sources represent the same real-world entity. We further employ a variety of ways to combine multiple classifiers for improved classification accuracy. In this paper, we report on some promising empirical results that demonstrate performance improvement by combining multiple classifiers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Systems - Volume 30, Issue 2, April 2005, Pages 119-132
Journal: Information Systems - Volume 30, Issue 2, April 2005, Pages 119-132
نویسندگان
Huimin Zhao, Sudha Ram,