دانلود مقاله ISI انگلیسی شماره 79619
عنوان فارسی مقاله

یادگیری مدت توزین از طریق برنامه نویسی ژنتیک برای طبقه بندی متن

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79619 2015 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Term-weighting learning via genetic programming for text classification
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 83, July 2015, Pages 176–189

کلمات کلیدی
یادگیری مدت توزین؛ برنامه نویسی ژنتیک؛ استخراج متن؛ آموزش نمایندگی؛ کیسه از کلمات
پیش نمایش مقاله
پیش نمایش مقاله یادگیری مدت توزین از طریق برنامه نویسی ژنتیک برای طبقه بندی متن

چکیده انگلیسی

This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWSs (e.g., Boolean and term-frequency schemes), the definition of TWSs has been traditionally an art. Further, it is still a difficult task to determine what is the best TWS for a particular problem and it is not clear yet, whether better schemes, than those currently available, can be generated by combining known TWS. We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification. The genetic program learns how to combine a set of basic units to give rise to discriminative TWSs. We report an extensive experimental study comprising data sets from thematic and non-thematic text classification as well as from image classification. Our study shows the validity of the proposed method; in fact, we show that TWSs learned with the genetic program outperform traditional schemes and other TWSs proposed in recent works. Further, we show that TWSs learned from a specific domain can be effectively used for other tasks.

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