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Associative classification using naive Bayes theorem
Other Title(s)
التصنيف المبني على الروابط باستخدام نظرية بايز
Dissertant
Thesis advisor
University
Philadelphia University
Faculty
Faculty of Information Technology
Department
Department of Computer Science
University Country
Jordan
Degree
Master
Degree Date
2014
English Abstract
-Associative classification usually generates a large set of rules.
Therefore, it is inevitable that an instance matches several rules which classes are conflicted, several associative classification based their prediction on one rule and ignore all other rules, even high confident ones, In this research, a new approach called Associative Classification using naïve Bayes (AC-NB) is proposed, which uses an improved naïve Bayes theorem to address these issues.
Our experiments on five UCI datasets show that AC-NB outperforms both RIPPER and NB on accuracy, also compared with new associative classification approaches; our proposed approach was highly competitive.
Main Subjects
Topics
No. of Pages
55
Table of Contents
Table of contents.
Abstract
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Related works.
Chapter Three : Asociation classification using NB.
Chapter Four : Experimental results.
Chapter Five : Conclusion and future works.
References.
American Psychological Association (APA)
Abu Jabir, Fawzi Ali. (2014). Associative classification using naive Bayes theorem. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-546116
Modern Language Association (MLA)
Abu Jabir, Fawzi Ali. Associative classification using naive Bayes theorem. (Master's theses Theses and Dissertations Master). Philadelphia University. (2014).
https://search.emarefa.net/detail/BIM-546116
American Medical Association (AMA)
Abu Jabir, Fawzi Ali. (2014). Associative classification using naive Bayes theorem. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-546116
Language
English
Data Type
Arab Theses
Record ID
BIM-546116