rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Arabic text classification based on artificial bee colony algorithm and semantic relations
  name:
    name part: Hijazi, Musab Mustafa
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Akram M. Zeki M Khedher
    role:
      role term: degree supervisor
  name:
    name part: Amelia Ritahani Ismail
    role:
      role term: degree supervisor
  name:
    name part: International Islamic University Malaysia
    name part: Kulliyyah of Information and Communication Technology
  type of resource: text
  genre: theses
  genre: Theses, IIUM local
  origin info:
    place:
      place term: my
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  language: languageTerm
  physical description:
    form: print
    extent: xxii, 158 leaves : illustrations ; 30cm.
  abstract: Documents contain a tremendous quantity of important human information. The use of automatic text classification is necessitated by the substantial increase in the volume of machine-readable documents for public or private access. Text classification is the process of categorizing or organizing documents into a predetermined set of classes. Western languages, namely English, have received a lot of attention, whereas the Arabic language has received far less attention. Arabic text categorization methods emerged spontaneously as a result of the vast volume of diverse textual material provided in Arabic on the internet. The selection of features is an essential step in text categorization. It is an important preprocessing approach for effective data analysis, in which just a subset of the original data features is chosen after eliminating noisy, unnecessary, or duplicated features. Bag of Words (BoWs) representation is considered the simplest representation of texts. Most Arabic researchers have been trying to find an accurate Arabic text classification based on the traditional Bag of Words (BoWs) for data representation which does not consider the semantic relationships between the words, such as synonymy and hypernyms. This research aims to build a model for Arabic text classification using the Artificial bee colony algorithm as a feature selection method and Arabic WordNet (AWN) as a lexical and semantic resource to utilize the semantic relationships between the words. The results of the research showed that the proposed Chi-square – Binary Artificial Bee Colony chi-BABC feature selection method was able to reduce the dimensionality of the feature set and at the same time improve the text classification. It was able to reduce approximately 89% of the original feature list size when the Naïve Bayes classifier was used as a fitness function. On the other hand, around 94% of the original feature list size was reduced by the proposed feature selection method when Suppor…
  target audience: specialized
  note: by Musab Mustafa Hijazi
  note: Abstracts in English and Arabic.
  note: "A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy in Computer Science." --On title page.
  note: Thesis (Ph.D)--International Islamic University Malaysia, 2022.
  note: Includes bibliographical references (leaves 131-157).
  subject:
    geographic code: a-my---
  identifier: http://studentrepo.iium.edu.my/handle/123456789/11438
  record info:
    record content source: UIAM
    record creation date: 230710
    language of cataloging:
      language term: eng