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Adel Taweel

Work place: Department of Computer Science, Birzeit University, Palestine

E-mail: ataweel@birzeit.edu

Website:

Research Interests: Computational Engineering, Software Construction, Software Development Process, Software Engineering

Biography

Adel Taweel is a faculty member of the Department of Computer Science at Birzeit University. He has previously held academic posts at the Universities of Keele, Manchester, Birmingham and King’s College London, where he still holds an academic position. He has led, managed and worked on several EU, USA and UK funded projects, including ePCRN, CLEF, CLEF-Services, and CLARiFi. Dr. Taweel is a Coordinator and Principal Investigator of three current projects (HiCure, Diet4Elders, IMI EHR4CR) and has previously co/principle investigated and led several other projects (FP7 TRANSFoRm, PEARL, ePCRN, ENJECT, HealthConnect). He is a Charted Engineer and member of a number of professional institutions (BCS, IET, IEEE), and has served as a member of several UK national committees and scientific consultant for several companies and government ministries include the UK Department of Health. Dr. Taweel is an expert EU reviewer and sits on the reviewer panel of several UK research councils. Dr. Taweel has published more than 90 peer-reviewed scientific publications in distributed software engineering, component-based, service-based and agent-based systems and medical informatics, and has served as a chair, a session chair of several international conferences and is a standing committee member, editorial member and reviewer of several books, and international journals and conferences.

Author Articles
Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

By Ahmed F. Alia Adel Taweel

DOI: https://doi.org/10.5815/ijitcs.2017.04.08, Pub. Date: 8 Apr. 2017

Feature Selection (FS) is an important process to find the minimal subset of features from the original data by removing the redundant and irrelevant features. It aims to improve the efficiency of classification algorithms. Rough set theory (RST) is one of the effective approaches to feature selection, but it uses complete search to search for all subsets of features and dependency to evaluate these subsets. However, the complete search is expensive and may not be feasible for large data due to its high cost. Therefore, meta-heuristics algorithms, especially Nature Inspired Algorithms, have been widely used to replace the reduction part in RST. This paper develops a new algorithm for Feature Selection based on hybrid Binary Cuckoo Search and rough set theory for classification on nominal datasets. The developed algorithm is evaluated on five nominal datasets from the UCI repository, against a number of similar NIAs algorithms. The results show that our algorithm achieves better FS compared to two known NIAs in a lesser number of iterations, without significantly reducing the classification accuracy.

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