Work place: Essam Al Daoud, Computer Science Department, Zarqa University, Zarqa, Jordan.
E-mail: essamdz@zu.edu.jo
Website:
Research Interests: Computational Learning Theory, Computer Architecture and Organization, Combinatorial Optimization
Biography
Essam Al Daoud received his BSc from Mu’tah university, MSc from Al Al-Bayt university, and his PhD in computer science from university putra malaysia in 2002. Currently, he is an associate professor in the computer science department at Zarqa university, Jordan. His research interests include machine learning, optimization quantum computation and cryptography.
DOI: https://doi.org/10.5815/ijmecs.2015.02.03, Pub. Date: 8 Feb. 2015
To increase learning accuracy, it is important to remove misleading, redundant, and irrelevant features. Fuzzy rough set offers formal mathematical tools to reduce the number of attributes and determine the minimal subset. Unfortunately, using the formal approach is time consuming, particularly if a large dataset is used. In this paper, an efficient algorithm for finding a reduct is introduced. Several techniques are proposed and combined with the harmony search, such as using a balanced fitness function, fusing the classical ranking methods with the fuzzy-rough method, and applying binary operations to speed up implementation. Comprehensive experiments on 18 datasets demonstrate the efficiency of using the suggested algorithm and show that the new algorithm outperforms several well-known algorithms.
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