کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
402786 677003 2013 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Knowledge-based instance selection: A compromise between efficiency and versatility
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Knowledge-based instance selection: A compromise between efficiency and versatility
چکیده انگلیسی

Traditionally, each instance selection proposal applies the same selection criterion to any problem. However, the performance of such criteria depends on the input data and a single one is not sufficient to guarantee success over a wide range of environments. An option to adapt the selection criteria to the input data is the use of meta-learning to build knowledge-based systems capable to choose the most appropriate selection strategy among several available candidates. Nevertheless, there is not in the literature a theoretical framework that guides the design of instance selection techniques based on meta-learning. This paper presents a framework for this purpose as well as a case study in which the framework is instantiated and an experimental study is carried out to show that the meta-learning approach offers a good compromise between efficiency and versatility in instance selection.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 47, July 2013, Pages 65–76
نویسندگان
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