کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
403429 677227 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Two feature weighting approaches for naive Bayes text classifiers
ترجمه فارسی عنوان
دو روش ارزش گذاری ویژگی برای طبقه بندی متن ساده Bayes
کلمات کلیدی
طبقه بندی متن متنوع Bayes؛ مقیاس ویژگی؛ نسبت جذب؛ درخت تصمیم گیری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

This paper works on feature weighting approaches for naive Bayes text classifiers. Almost all existing feature weighting approaches for naive Bayes text classifiers have some defects: limited improvement to classification performance of naive Bayes text classifiers or sacrificing the simplicity and execution time of the final models. In fact, feature weighting is not new for machine learning community, and many researchers have made fruitful efforts in the field of feature weighting. This paper reviews some simple and efficient feature weighting approaches designed for standard naive Bayes classifiers, and adapts them for naive Bayes text classifiers. As a result, this paper proposes two adaptive feature weighting approaches for naive Bayes text classifiers. Experimental results based on benchmark and real-world data show that, compared to their competitors, our feature weighting approaches show higher classification accuracy, yet at the same time maintain the simplicity and lower execution time of the final models.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 100, 15 May 2016, Pages 137–144
نویسندگان
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