An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.

روابط وملفات

التعريف والنوع

رقم الوثيقة

fb7bffff-4e74-437a-9da8-a8b71f9154f5

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

1700728_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"عثمان سوفان","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"فلاديمير باجيك","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

dc.contributor.advisor Bajic, Vladimir B. dc.contributor.author Soufan, Othman dc.date.accessioned 2012-09-18T10:14:15Z dc.date.available 2012-09-18T10:14:15Z dc.date.issued 2012-09 dc.identifier.doi 10.25781/KAUST-R9Q03 dc.identifier.uri http://hdl.handle.net/10754/244576 dc.description.abstract Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics. dc.language.iso en

إشراف وإعداد

الإشراف

فلاديمير باجيك

الإعداد

عثمان سوفان

الاقتباسات الببليوغرافية