Deanship of Graduate Studies | Researches | Multilabel Classification Model for Streaming Data Adaptive Model Rule for Multilabel Streaming Data (AMLSD(

Main Page
Deanship
- The Dean
  - Dean's Word
  - Curriculum Vitae
  - Contact the Dean
- Vision and Mission
- Organizational Structure
- Vice- Deanship
- Vice- Dean
- KAU Graduate Studies
Research Services & Courses
- Research Services Unit
- Important Research for Society
- Deanship's Services
  - FAQs
  - Research
  - Staff Directory
  - Files
  - Favorite Websites
  - Deanship Access Map
Graduate Studies Awards
Deanship's Staff
- Staff Directory
Files
Researches
Contact us

- عربي
- English

Deanship of Graduate Studies

Document Details

Document Type	:	Thesis
Document Title	:	Multilabel Classification Model for Streaming Data Adaptive Model Rule for Multilabel Streaming Data (AMLSD( نموذج التصنيف متعدد التسميه للبيانات المتدفقة
Subject	:	Faculty of Computing and Information Technology
Document Language	:	Arabic
Abstract	:	challenge with processing multilabel streaming data (MLSD) is implementing a stable and adaptive model; a model could deal with all the challenges, issues and unique features such model may have. An adaptive model rule was implemented in this study to process the multilabel streaming data (MLSD), the model integrated of multi-phases work together to process the MLSD with stability and adaptive manner. Adaptive Model Rule model is implemented in this study to process the MLSD. The main component of AMLSD is the aggregator. The aggregator would hold the main functionalities of the AMLSD model such as the testing and learning processes. AMRule is one of used functions which aim to minimize the training time by collected the predicted rules over the training instances in the rule set (RS). Another important part is the page-Hinkely (PH) test which work after the AMRule to detect the outliers of statistics and heuristically values of the current sketch of instances, a decision of expanding the RS or not would be execute according to the PH test. The AMLSD performance behaviour is evaluate and test over various setting and parameters in term of comparative statements with other scenarios. These comparative statements are illustrated though a multi-comparative statement using different performance metrics to determine the benefits of using the AMLSD with the multilabel streaming data (MLSD).
Supervisor	:	Prof. Wadee Alhlabi
Thesis Type	:	Master Thesis
Publishing Year	:	1441 AH 2020 AD
Added Date	:	Tuesday, June 2, 2020

Researchers

Researcher Name (Arabic)	Researcher Name (English)	Researcher Type	Dr Grade	Email
أماني العطاس	alattas, Amani	Researcher	Master

Files

File Name	Type	Description
46263.pdf	pdf

Back To Researches Page