Support vector machine text classifier for Arabic articles : ant colony optimization based feature subset selection

مقدم أطروحة جامعية

Muslih, Abd al-wadud Muhammad Abd al-Wadud

مشرف أطروحة جامعية

Kanan, Ghassan Jaddu

أعضاء اللجنة

al-Shalabi, Riyad
Abu Shawir, Bayan
al-Arif, Taha

الجامعة

الأكاديمية العربية للعلوم المالية و المصرفية

الكلية

كلية نظم و تكنولوجيا المعلومات

القسم الأكاديمي

قسم نظم المعلومات الحاسوبية

دولة الجامعة

الأردن

الدرجة العلمية

دكتوراه

تاريخ الدرجة العلمية

2008

الملخص الإنجليزي

In this thesis, we have implemented a support vector machine (SVM) text classifier for Arabic articles.

Experimental results show that the SVM classifier outperformed Naive Bayesian (NB) and k-nearest neighbor (kNN) classifiers.

We investigated the effectiveness of six state-of-the-art feature subset selection (FSS) methods, which are commonly used in text classification (TC) tasks, for our Arabic SVM text classification system.

We implemented an Ant Colony Optimization Based-Feature Subset Selection (ACO Based-FSS) method for our Arabic SVM text classifier.

The proposed FSS method adapted Chi-square statistic as heuristic information and the effectiveness of the SVM classifier as a guide to improving the selection of features for each category.

Compared to the six state-of-the-art FSS methods, our ACO Based-FSS algorithm achieved better TC effectiveness.

Evaluation used an in-house Arabic TC corpus1 that consists of 1445 documents independently classified into nine categories.

The experimental results were presented in terms of macro-averaging precision, macro averaging recall and macro-averaging F1 measures.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الخوارزميات

عدد الصفحات

114

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-306127