Novel supervised and semi-supervised learning methods for handling class imbalance in customer churn prediction / (Record no. 179158)

MARC details
000 -LEADER
fixed length control field 04727namaa22004331i 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-GICUC
005 - أخر تعامل مع التسجيلة
control field 20260415131718.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260402s2025 ua a|||frm||| 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloguing agency EG-GICUC
Language of cataloging eng
Transcribing agency EG-GICUC
Modifying agency EG-GICUC
Description conventions rda
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
Language code of summary or abstract eng
-- ara
049 ## - Acquisition Source
Acquisition Source Deposit
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.1
092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC)
Classification number 005.1
Edition number 21
097 ## - Degree
Degree Ph.D
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Local Call Number Cai01.13.06.Ph.D.2025.Sa.N
100 0# - MAIN ENTRY--PERSONAL NAME
Authority record control number or standard number Salma Abdelmonem Abdelmotaleb Mohamed,
Preparation preparation.
245 10 - TITLE STATEMENT
Title Novel supervised and semi-supervised learning methods for handling class imbalance in customer churn prediction /
Statement of responsibility, etc. by Salma Abdelmonem Abdelmotaleb Mohamed ; Supervisors Prof. Samir Ibrahim Shaheen, Dr. Dina Ahmed Mohamed Elreedy.
246 15 - VARYING FORM OF TITLE
Title proper/short title طرق مبتكرة للتعلم من البيانات المصنفة و البيانات شبه المصنفة في ظل عدم توازن البيانات وتطبيقها في توقع مغادرة العملاء
264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice 2025.
300 ## - PHYSICAL DESCRIPTION
Extent 106 pages :
Other physical details illustrations ;
Dimensions 30 cm. +
Accompanying material CD.
336 ## - CONTENT TYPE
Content type term text
Source rda content
337 ## - MEDIA TYPE
Media type term Unmediated
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Source rdacarrier
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Ph.D)-Cairo University, 2025.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Bibliography: pages 95-106.
520 #3 - SUMMARY, ETC.
Summary, etc. Customer churn prediction involves identifying which customers are likely<br/>to leave or discontinue using a service. While using machine learning tech-<br/>niques can be beneficial for this prediction, there are some challenges that<br/>arise from the characteristics of the datasets involved. In this thesis, we<br/>present novel two supervised and semi-supervised approaches for learning<br/>from class-imbalanced datasets like in customer churn prediction applica-<br/>tions. First, we propose a novel algorithm-level adaptation to the supervised<br/>Gaussian Process Classifier (CIRA) which can effectively learn from unbal-<br/>anced datasets. Second, we propose a class imbalanced safe semi-supervised<br/>approach (CISL) in a secure advanced self-training approach which can suc-<br/>cessfully acquire knowledge from the limited labeled imbalanced datasets. We<br/>conduct experiments on imbalanced benchmark datasets and real customer<br/>churn prediction datasets. The experimental results, supported with statis-<br/>tical significance tests, demonstrate consistent performance enhancements<br/>using different performance measures.
520 #3 - SUMMARY, ETC.
Summary, etc. في هذه الرسالة، نقدم طرقًا جديدة في التعلم الآلي باستخدام البيانات المكتملة والبيانات غير المكتملة للتعلم الآلي برغم عدم توازن البيانات كما هو الحال في تطبيقات توقع مغادرة العملاء. أولاً، نقدم تعديلًا جديدًا على خوارزمية مشهورة في التعلم الآلي باستخدام البيانات المكتملة. يُمَكِن التعديل المقترح، الخوارزمية من التعلم بفعالية من مجموعات البيانات غير المتوازنة. ثانيًا، نقترح طريقة آمنة للتعلم من البيانات غير المكتملة وغير المتوازنة بحيث تكون كفاءة البرنامج مضمونة مسبقًا. للتحقق من كفاءة الطرق المقدمة، أجرينا تجارب على مجموعات بيانات مكتملة غير متوازنة، ومجموعات بيانات غير مكتملة وغير متوازنة، ومجموعات بيانات حقيقية شهيرة لتوقع مغادرة العملاء. تُظهر النتائج التجريبية، المدعومة باختبارات الدلالة الإحصائية، تحسينات مستمرة وثابتة في الأداء باستخدام معايير قياس أداء مختلفة.
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Issues CD Issues also as CD.
546 ## - LANGUAGE NOTE
Text Language Text in English and abstract in Arabic & English.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer Engineering
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element هندسة الحاسبات
653 #1 - INDEX TERM--UNCONTROLLED
Uncontrolled term Customer Churn Prediction
-- Class imbalance
-- Supervised Learning
-- Semi- supervised Learning
-- Safe Semi-supervised Learning
-- Gaussian Process Clas- sifier
-- Self Training
-- Pseudo Labeling
-- Ratio Sampling
-- Safety Checking
-- التعلم الآلي
-- التعلم من البيانات المكتملة
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Samir Ibrahim Shaheen
Relator term thesis advisor.
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Dina Ahmed Mohamed Elreedy
Relator term thesis advisor.
900 ## - Thesis Information
Grant date 01-01-2025
Supervisory body Samir Ibrahim Shaheen
-- Dina Ahmed Mohamed Elreedy
Discussion body Reda Abdel-Wahab El-Khoribi
-- Mohamed Zaki Abdelmegeed
Universities Cairo University
Faculties Faculty of Engineering
Department Department of Computer Engineering
905 ## - Cataloger and Reviser Names
Cataloger Name Shimaa
Reviser Names Eman Ghareb
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
Edition 21
Suppress in OPAC No
Holdings
Source of classification or shelving scheme Home library Current library Date acquired Inventory number Full call number Barcode Date last seen Effective from Koha item type
Dewey Decimal Classification المكتبة المركزبة الجديدة - جامعة القاهرة قاعة الرسائل الجامعية - الدور الاول 02.04.2026 93649 Cai01.13.06.Ph.D.2025.Sa.N 01010110093649000 02.04.2026 02.04.2026 Thesis
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