Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (7): 82-89    DOI: 10.11925/infotech.2096-3467.2017.07.10
Orginal Article Current Issue | Archive | Adv Search |
Feature Selection Based on Modified QPSO Algorithm
Zhipeng Li(),Weizhong Li
Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Download: PDF(724 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study proposes an algorithm for feature selection aiming to improve the precision and efficiency of text classification. [Methods] First, we selected features based on their characteristics. Then, we constructed the algorithm with extension theory to strengthen its searching ability. Finally, we compared the performance of different methods for text classification. [Results] Compared with IG, MI and QPSO, the proposed algorithm had better accuracy in feature selection. [Limitations] The efficiency of our algorithm needs to be improved. [Conclusions] The modified QPSO Algorithm is an effective way to select features.

Key wordsFeature Selection      Quantum-behaved Particle Swarm      Extenics      Niche      Fitness Sharing     
Received: 27 May 2017      Published: 13 September 2017

Cite this article:

Zhipeng Li,Weizhong Li. Feature Selection Based on Modified QPSO Algorithm. Data Analysis and Knowledge Discovery, 2017, 1(7): 82-89.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.07.10     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I7/82

[1] 何熊熊, 管俊轶, 叶宣佐. 一种基于密度和网格的簇心可确定聚类算法[J]. 控制与决策, 2017, 32(5): 913-919.
[1] (He Xiongxiong, Guan Junyi, Ye Xuanzuo.A Density-based and Grid-based Cluster Centers Determination Clustering Algorithm[J]. Control and Decision, 2017, 32(5): 913-919.)
[2] 任俊亮, 邢清华, 李强, 等. 采用自适应概率粒子群算法的反导预警资源调度方法[J]. 空军工程大学学报: 自然科学版, 2014, 15(6): 45-48.
[2] (Ren Junliang, Xing Qinghua, Li Qiang, et al.Resource Scheduling Method of Missile Defense Ear1y Warning System Based on Self-Adaptive Probability Particle Swam Optimization[J]. Journal of Air Force Engineering University: Natural Science Edition, 2014, 15(6): 45-48.)
[3] Sun J, Feng B, Xu W B.Particle Swarm Optimization with Particle Having Quantum Behavior [C]//Proceedings of Congress on Evolutionary Computation, Portland.USA: IEEE Press, 2004, 1: 325-331.
[4] Sun J, Xu W B, Feng B.Adaptive Parameter Control for Quantum Behaved Particle Swarm Optimization on Individual Level[C]//Proceedings of IEEE International Conference on Systems, Man and Cybernetics. Piscataway: IEEE Press, 2005: 3049-3054.
[5] 路永和, 梁明辉. 遗传算法在改进文本特征提取方法中的应用[J]. 现代图书情报技术, 2014(4): 48-57.
[5] (Lu Yonghe, Liang Minghui.Improvement of Text Feature Extraction with Genetic Algorithm[J]. New Technology of Library and Information Service, 2014(4): 48-57.)
[6] 刘逵, 周竹荣. 基于野草算法的文本特征选择[J]. 计算机应用, 2012, 32(8): 2245-2249.
[6] (Liu Kui, Zhou Zhurong.Text Feature Selection Method Based on Invasive Weed Optimization[J]. Journal of Computer Applications, 2012, 32(8): 2245-2249.)
[7] 林令娟, 刘希玉. 基于微粒群优化的快速K-近邻分类算法[J]. 山东科学, 2009, 22(1): 13-16.
[7] (Lin Lingjuan, Liu Xiyu.A Particle Swarm Optimization Based Rapid K-nearest Neighbor Classification Algorithm[J]. Shandong Science, 2009, 22(1): 13-16.)
[8] 李欢, 焦建民. 简化的粒子群优化快速KNN分类算法[J]. 计算机工程与应用, 2008, 44(32): 57-59.
[8] (Li Huan, Jiao Jianmin.Improved Simplified PSO KNN Classification Algorithm[J]. Computer Engineering and Applications, 2008, 44(32): 57-59.)
[9] 拓守恒. 基于改进PSO的SVM文本分类研究[J]. 电脑开发与应用, 2010, 23(10): 3-5, 8.
[9] (Tuo Shouheng.Research on Text Categorization Based on Support Vector Machine Optimized by Particle Swarm Optimization Algorithm[J]. Computer Development & Applications, 2010, 23(10): 3-5, 8.)
[10] 孙洋. 粒子群算法的改进及其在文本分类上的应用[J]. 中央民族大学学报: 自然科学版, 2008, 17(3): 57-62.
[10] (Sun Yang.The Improvement of PSO Algorithm and Application of Text Classifier[J]. Journal of the Central University for Nationalities: Natural Sciences Edition, 2008, 17(3): 57-62.)
[11] 徐辉. 基于混沌二进制粒子群优化的KNN文本分类算法[J]. 微电子学与计算机, 2012, 29(8): 204-208.
[11] (Xu Hui.KNN Text Classification Algorithm Based on Chaotic Binary Particle Swarm Optimization[J]. Microelectronics & Computer, 2012, 29(8): 204-208.)
[12] 谭德坤. 基于混沌微粒群算法的文本分类研究[J]. 计算机应用研究, 2010, 27(12): 4464-4466.
[12] (Tan Dekun.Research of Chinese Text Categorization Based on Chaotic Particle Swarm Optimization[J]. Application Research of Computers, 2010, 27(12): 4464-4466.)
[13] 朱颢东, 钟勇. 基于并行二进制免疫量子粒子群优化的特征选择方法[J]. 控制与决策, 2010, 25(1): 53-63.
[13] (Zhu Haodong, Zhong Yong.Feature Selection Method Based on PBIQPSO[J]. Control and Decision, 2010, 25(1): 53-63.)
[14] 孔莉芳, 张虹. 用于特征子集选择的异步并行微粒群优化方法[J]. 控制与决策, 2012, 27(7): 967-973.
[14] (Kong Lifang, Zhang Hong.Asynchronous Parallel Particle Swarm Optimizer for Feature Subset Selection[J]. Control and Decision, 2012, 27(7): 967-973.)
[15] 巩敦卫, 胡滢, 张勇. 基于多目标微粒群优化的异质数据特征选择[J]. 电子学报, 2014, 42(7): 1320-1326.
[15] (Gong Dunwei, Hu Ying, Zhang Yong.Feature Selection of Heterogeneous Data Based on Multi-objective Particle Swarm Optimization[J]. Acta Electronica Sinica, 2014, 42(7): 1320-1326.)
[16] 付强, 王刚, 王明宇, 等. 基于小生境遗传算法的制导雷达误差估计[J]. 空军工程大学学报: 自然科学版, 2011, 11(6): 50-53.
[16] (Fu Qiang, Wang Gang, Wang Mingyu, et al.Research of Guidance Radar Error Estimation Based on the Niche Genetic Algorithm[J]. Journal of Air Force Engineering University: Natural Science Edition, 2011, 11(6): 50-53.)
[17] 杨春燕, 蔡文. 可拓学[M]. 北京: 科学出版社, 2014: 18-96.
[17] (Yang Chunyan, Cai Wen.Extenics[M]. Beijing: Science Press, 2014: 18-96.)
[18] 赵敏, 林道荣, 瞿波, 等. 一种新的基于小生境模拟退火的遗传算法[J].辽宁工程技术大学学报: 自然科学版, 2013, 32(3): 367-372.
[18] (Zhao Min, Lin Daorong, Qu Bo, et al.A New Genetic Algorithm Based on Niche Simulated Annealing[J]. Journal of Liaoning Technical University: Natural Science, 2013, 32(3): 367-372.)
[19] 李中华, 张泰山. 可拓聚类适应度共享小生境遗传算法研究[J]. 哈尔滨工业大学学报, 2016, 48(5): 178-183.
[19] (Li Zhonghua, Zhang Taishan.Research of Fitness Sharing Niche Genetic Algorithms Based on Extension Clustering[J]. Journal of Harbin Institute of Technology, 2016, 48(5): 178-183.)
[20] 曾维宏. 基于粗糙集理论的数据挖掘算法研究[D]. 郑州: 郑州大学, 2005.
[20] (Zeng Weihong.Research of Reduction Algorithm Based on Rough Set Theory [D]. Zhengzhou: Zhengzhou University, 2005.)
[21] 张珂, 黄永峰, 李星. 一种基于适应度和节点聚类的P2P拓扑建模方法[J]. 电子学报, 2010, 38(7): 1634-1640.
[21] (Zhang Ke, Huang Yongfeng, Li Xing.A Model for Topology of P2P Network Based on Fitness and Node Clustering[J]. Acta Electronica Sinica, 2010, 38(7): 1634-1640.)
[22] 谭熠峰, 孙婷婷, 徐新民. 基于动态因子和共享适应度的改进粒子群算法[J]. 浙江大学学报: 理学版, 2016, 43(6): 696-700.
[22] (Tan Yifeng, Sun Tingting, Xu Xinmin.A Modified Particle Swarm Optimization Algorithm Based on Dynamic Learning Factors and Sharing Method[J]. Journal of Zhejiang University: Science Edition, 2016, 43(6): 696-700.)
[23] 邵鹏, 吴志健, 周炫余, 等. 基于折射原理反向学习模型的改进粒子群算法[J]. 电子学报, 2015, 43(11): 2137-2144.
[23] (Shao Peng, Wu Zhijian, Zhou Xuanyu, et al.Improved Particle Swarm Optimization Algorithm Based on Opposite Learning of Refraction[J]. Acta Electronica Sinica, 2015, 43(11): 2137-2144.)
[1] Cheng Zhou,Hongqin Wei. Evaluating and Classifying Patent Values Based on Self-Organizing Maps and Support Vector Machine[J]. 数据分析与知识发现, 2019, 3(5): 117-124.
[2] Jiaming Liang,Jie Zhao,Zhou Jianlong,Zhenning Dong. Detecting Collusive Fraudulent Online Transaction with Implicit User Behaviors[J]. 数据分析与知识发现, 2019, 3(5): 125-138.
[3] Tingxin Wen,Yangzi Li,Jingshuang Sun. News Hotspots Discovery Method Based on Multi Factor Feature Selection and AFOA/K-means[J]. 数据分析与知识发现, 2019, 3(4): 97-106.
[4] Zhanglu Tan,Zhaogang Wang,Han Hu. Study on a Method of Feature Classification Selection Based on χ2 Statistics[J]. 数据分析与知识发现, 2019, 3(2): 72-78.
[5] Tingxin Wen,Yangzi Li,Jingshuang Sun. Extracting Text Features with Improved Fruit Fly Optimization Algorithm[J]. 数据分析与知识发现, 2018, 2(5): 59-69.
[6] Yue Zhang,Dongbo Wang,Danhao Zhu. Segmenting Chinese Words from Food Safety Emergencies[J]. 数据分析与知识发现, 2017, 1(2): 64-72.
[7] Xiangdong Li,Tao Ruan,Kang Liu. Automatic Classification of Documents from Wikipedia[J]. 数据分析与知识发现, 2017, 1(10): 43-52.
[8] Yonghe Lu,Jinghuang Chen. Optimizing Feature Selection Method for Text Classification with Shuffled Frog Leaping Algorithm[J]. 数据分析与知识发现, 2017, 1(1): 91-101.
[9] Liu Hongguang,Ma Shuanggang,Liu Guifeng. Classifying Chinese News Texts with Denoising Auto Encoder[J]. 现代图书情报技术, 2016, 32(6): 12-19.
[10] Meng Yuan,Wang Hongwei. Evaluating Online Reviews Based on Text Content Features[J]. 现代图书情报技术, 2016, 32(4): 40-47.
[11] Gao Feng, Xiong Jing, Liu Yongge. Research on the Extenics of Oracle Bone Inscriptions Interpretation Based on HowNet[J]. 现代图书情报技术, 2015, 31(7-8): 58-64.
[12] Li Gang, Ye Guanghui, Zhang Yan. Feature Recognition of Niche Expert——Empirical Analysis Based on MetaFilter Dataset[J]. 现代图书情报技术, 2015, 31(6): 71-77.
[13] Li Xiangdong, Ba Zhichao, Huang Li. Allocation and Multi-granularity[J]. 现代图书情报技术, 2015, 31(5): 42-49.
[14] Xu Dongdong, Wu Shaobo. An Improved TF-IDF Feature Selection Based on Categorical Description[J]. 现代图书情报技术, 2015, 31(3): 39-48.
[15] Tan Xueqing, Zhou Tong, Luo Lin. A Text Classification Algorithm Based on the Average Category Similarity[J]. 现代图书情报技术, 2014, 30(9): 66-73.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn