کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
390627 661277 2009 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems
چکیده انگلیسی

The performance of information retrieval systems (IRSs) is usually measured using two different criteria, precision and recall. Precision is the ratio of the relevant documents retrieved by the IRS in response to a user's query to the total number of documents retrieved, whilst recall is the ratio of the number of relevant documents retrieved to the total number of relevant documents for the user's query that exist in the documentary database. In fuzzy ordinal linguistic IRSs (FOLIRSs), where extended Boolean queries are used, defining the user's queries in a manual way is usually a complex task. In this contribution, our interest is focused on the automatic learning of extended Boolean queries in FOLIRSs by means of multi-objective evolutionary algorithms considering both mentioned performance criteria. We present an analysis of two well-known general-purpose multi-objective evolutionary algorithms to learn extended Boolean queries in FOLIRSs. These evolutionary algorithms are the non-dominated sorting genetic algorithm (NSGA-II) and the strength Pareto evolutionary algorithm (SPEA2).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 160, Issue 15, 1 August 2009, Pages 2192-2205