Attribute Selection Method Based on Improved Discrete Glowworm Swarm Optimization and Fractal Dimension
NI Zhi-Wei, XIAO Hong-Wang, WU Zhang-Jun, XUE Yong-Jian
School of Management, Hefei University of Technology, Hefei 230009 Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009
Abstract Attribute selection is an important method of data preprocessing in the field of data mining. An improved attribute selection method is proposed which combines discrete glowworm swarm optimization (DGSO) algorithm with fractal dimension. In this method,fractal dimension is taken as the evaluation criteria for attribute subsets and DGSO algorithm as a kind of search strategy. To analyze the feasibility and the effectiveness of the proposed method,six UCI datasets are used in the experiments,and the 10-fold cross validation and support vector machine algorithm are utilized to evaluate the classification accuracy before and after attribute selection. Then, different evaluation criteria and search strategies are compared and the parameters are analyzed in detail. The experimental results show that the proposed method has comparatively high feasibility and effectiveness.
[1] Wang Xiangyang, Yang Jie, Teng Xiaolong, et al. Feature Selection Based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, 2007, 28(4): 459-471 [2] Jensen R, Shen Qiang. Finding Rough Set Reducts with Ant Colony Optimization // Proc of the UK Workshop on Computational Intelligence. Bristol, UK, 2003: 15-22 [3] Liu Hongbo, Abraham A, Li Yanheng. Nature Inspired Population-Based Heuristics for Rough Set Reduction // Abraham A, Falcon R, Bello R, eds. Rough Set Theory: A True Landmark in Data Analysis. Berlin, Germany: Springer-Verlag, 2009, 174: 261-278 [4] Suguna N, Thanuskodi K. A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization. Journal of Computing, 2010, 2(6): 49-54 [5] Traina C, Traina A, Wu L, et al. Fast Feature Selection Using Fractal Dimension // Proc of the 15th Brazilian Symposium on Database. Joao Pessoa, Brazil, 2000: 78-90 [6] Eneva E, Kumaraswamy K, Matteucci M. WEKKEM:A Study in Fractal Dimension and Dimensionality Reduction[EB/OL]. [2013-02-20]. http://chrome.ws.dei.polimi.it/index.php/publications [7] Bao Yubin, Wang Zhuo, Sun Huangliang, et al. Fast Attribute Selection Algorithm Based on Fractal Dimension. Journal of Northeastern University: Natural Science, 2003, 24(6): 527-530 (in Chinese) (鲍玉斌,王 琢,孙焕良,等.一种基于分形维的快速属性选择算法.东北大学学报:自然科学版, 2003, 24(6): 527-530) [8] Yan Guanghui. Performance Optimization of Fractal Dimensionality Reduction Algorithm. Journal of Lanzhou Jiaotong University, 2007, 26(1): 6-10, 25 (in Chinese) (闫光辉.一种高效的分形属性选择算法.兰州交通大学学报, 2007, 26(1): 6-10, 25) [9] Ni Liping, Ni Zhiwei, Wu Hao, et al. Feature Selection Method Based on Fractal Dimension and Ant Colony Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 2009, 22(2): 293-298 (in Chinese) (倪丽萍,倪志伟,吴 昊,等.基于分形维数和蚁群算法的属性选择方法.模式识别与人工智能, 2009, 22(2): 293-298) [10] Banati H, Bajaj M. Firefly Based Feature Selection Approach. International Journal of Computer Science, 2011, 8(4): 473-480 [11] Traina Jr C, Traina A, Faloutsos C. Fast Feature Selection Using Fractal Dimension-Ten Years Later. Journal of Information and Data Management, 2010, 1(1): 17-20 [12] Krishnanand K N, Ghose D. Glowworm Swarm Optimization: A New Method for Optimizing Multimodal Functions. International Journal of Computational Intelligence Studies, 2009, 1(1): 93-119 [13] Krishnanand K N, Ghose D. Glowworm Swarm Optimization for Simultaneous Capture of Multiple Local Optima of Multimodal Functions. Swarm Intelligence, 2009, 3(2): 87-124 [14] Krishnanand K N, Ghose D. Glowworm Swarm Based Optimization Algorithm for Multimodal Functions with Collective Robotics Applications. Multiagent and Grid Systems, 2006, 2(3): 209-222 [15] Huang Zhengxing, Zhou Yongquan. Selp-Adaptive Step Glowworm Swarm Optimization Algorithm for Optimizing Multimodal Functions. Computer Science, 2011, 38(7): 220-224 (in Chinese) (黄正新,周永权.自适应步长萤火虫群多模态函数优化算法.计算机科学, 2011, 38(7): 220-224) [16] Huang Zhengxing, Zhou Yongquan. Adaptive Glowworm Swarm Optimization Algorithm with Changing Step for Optimizing Multimodal Functions. Computer Engineering and Applications, 2012, 48(8): 43-47 (in Chinese) (黄正新,周永权.变步长自适应萤火虫群多模态函数优化算法.计算机工程与应用, 2012, 48(8): 43-47) [17] Zhou Yongquan. Discrete Glowworm Swarm Optimization Algorithm for TSP Problem. Acta Electronica Sinica, 2012, 40(6): 1164-1170 (in Chinese) (周永权.求解TSP问题的离散型萤火虫群优化算法.电子学报, 2012, 40(6): 1164-1170) [18] Gong Qiaoqiao, Zhou Yongquan, Luo Qifang. Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Multidimensional Knapsack Problem.Procedia Engineering, 2011, 15: 2880-2884 [19] Gong Qiaoqiao, Zhou Yongquan, Yang Yan. Artificial Glowworm Swarm Optimization Algorithm for Solving 0-1 Knapsack Problem. Advanced Materials Research, 2011. DOI: 10.4028www.scientific.net/AMR.143-144.166 [20] Krishnanand K N, Ghose D. Detection of Multiple Source Locations Using a Glowworm Metaphor with Applications to Collective Robotics // Proc of the IEEE Swarm Intelligence Symposium. Pasadena, USA, 2005: 84-91 [21] Krishnanand K N, Ghose D. A Glowworm Swarm Optimization Based Multi-Robot System for Signal Source Localization // Liu Dikai, Wang Lingfeng, Kaychen T, eds. Design and Control of Intelligent Robotic Systems. Berlin, Germany: Springer-Verlag, 2009, 177: 53-74 [22] Traina Jr C, Traina A J M, Faloutsos C. Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees // Proc of the 16th IEEE International Conference on Data Engineering. San Diego, USA, 2000. DOI: 10.1109/ICDE.2000.839409 [23] Yang X S. Nature-Inspired Metaheuristic Algorithms. Frome, UK: Luniver Press, 2008 [24] Yang X S. Firefly Algorithms for Multimodal Optimization // Watanabe O, Zeugmann T, eds. Stochastic Algorithms: Foundations and Applications. Berlin, Germany: Springer-Verlag, 2009, 5792: 169-178
2013, Vol. 26 
