2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (2012)
Nov. 26, 2012 to Nov. 28, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ACSAT.2012.29
This paper presents a hybrid approach for solving null values problem, it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estimation through working with rough set theory. The results obtained from most code sets show that Bees algorithm better than ID3 in decreasing the number of extracted rules without affecting the accuracy and increasing the accuracy ratio of null values estimation, especially when the number of null values is increasing.
data handling, feature extraction, learning (artificial intelligence), rough set theory, search problems, statistical analysis, swarm intelligence
A. T. Sadiq, M. G. Duaimi and S. A. Shaker, "Data Missing Solution Using Rough Set theory and Swarm Intelligence," 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuala Lumpur, 2013, pp. 173-180.