Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Computational Models for Life Sciences
Guest editors: Tuan Pham and Xiaobo Zhou
Article type: Research Article
Authors: Crossingham, Bodie | Marwala, Tshilidzi; *
Affiliations: School of Electrical and Information Engineering, University of the Witwatersrand, Private Bag X3; WITS, 2050; South Africa
Correspondence: [*] Corresponding author. Tel.: +27 117 177 217; Fax: +27 114 031 929; E-mail: [email protected]
Abstract: Rough set theory (RST) is concerned with the formal approximation of crisp sets and is a mathematical tool which deals with vagueness and uncertainty. This paper presents an approach to optimize rough set partition sizes using various optimization techniques. The forecasting accuracy is measured by using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The four optimization techniques used are genetic algorithm, particle swarm optimization, hill climbing and simulated annealing. This proposed method is tested on two data sets, namely, the human immunodeficiency virus (HIV) data set and the militarized interstate dispute (MID) data set. The results obtained from this granulization method are compared to two previous static granulization methods, namely, equal-width-bin and equal-frequency-bin partitioning. The results conclude that all of the proposed optimized methods produce higher forecasting accuracies than that of the two static methods. In the case of the HIV data set, the hill climbing approach produced the highest accuracy; an accuracy of 69.02% is achieved in a time of 210.4 hours. For the MID data, the genetic algorithm approach produced the highest accuracy. The accuracy achieved is 95.82% in a time of 7 hours. The rules generated from the rough set are linguistic and easy-to-interpret, but this does come at the expense of the accuracy lost in the discretization process where the granularity of the variables is decreased.
Keywords: Combinatorial optimisation, conflict analysis, Discretization, HIV analysis, rough set theory
DOI: 10.3233/HIS-2008-5406
Journal: International Journal of Hybrid Intelligent Systems, vol. 5, no. 4, pp. 219-236, 2008
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]