کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
725590 892541 2015 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm
ترجمه فارسی عنوان
الگوریتم ژنتیک مرتب سازی غالب بدون استفاده از چند هدفه
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی

This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA - II). The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Applied Research and Technology - Volume 13, Issue 1, February 2015, Pages 145–159
نویسندگان
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