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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 81-87.doi: 10.6040/j.issn.1672-3961.0.2017.412

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基于蚁群算法求解Choquet模糊积分模型

陈嘉杰,王金凤*   

  1. 华南农业大学数学与信息学院, 广东 广州 510642
  • 收稿日期:2017-05-09 出版日期:2018-06-20 发布日期:2017-05-09
  • 通讯作者: 王金凤(1978— ),女,河北黄骅人,副教授,博士,主要研究方向为数据挖掘,机器学习. E-mail:wangphoenix@163.com E-mail:chen_jia_jie@sina.cn
  • 作者简介:陈嘉杰(1993— ),男,广东东莞人,硕士研究生,主要研究方向为模糊积分. E-mail:chen_jia_jie@sina.cn
  • 基金资助:
    国家自然科学基金资助项目(61202295);广东省公益研究与能力建设基金资助项目(2017A040406023);广东省公益研究与能力建设基金资助项目(2015A030401081)

Method for solving Choquet integral model based on ant colony algorithm

CHEN Jiajie, WANG Jinfeng*   

  1. College of Mathematics and Information, South China Agricultural University, Guangzhou 510642, Guangdong, China
  • Received:2017-05-09 Online:2018-06-20 Published:2017-05-09

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摘要： 为了提高Choquet模糊积分模糊测度的搜索效率,提出改进的蚁群算法求解模型。根据特征数量构建Choquet模糊积分模型,搜索过程中对每只蚂蚁按状态转移概率进行全局搜索或局部搜索,迭代搜索最优解,并由Fisher判别进行分类。试验使用3组癌症基因数据集,利用R语言的Bioconductor工具箱进行数据预处理,并分析对比新模型和主流算法的分类效果。结果表明:在DLBCL数据集和Colon数据集中,基于蚁群算法的Choquet模糊积分得到最好的分类效果;在Prostate数据集中,虽然和基于遗传算法的Choquet模糊积分分类效果接近,但是蚁群算法仍然很快收敛,改进的蚁群算法可以作为求解模糊测度的快速方法。

关键词: Choquet模糊积分, 模糊测度, 蚁群算法, 癌症分类, 遗传算法

Abstract: An improved ant colony algorithm for Choquet integral was investigated to enhance the search efficiency of fuzzy measure. Choquet integral model was built according to the characteristic quantity and solved by the process of searching globally or locally according to the state transition probability. It was classified by Fisher discriminates. The experiment used three sets of cancer gene datasets preprocessed by R language Bioconductor toolkit, and classification results was analyzed between new model and the mainstream algorithm. The results showed that in DLBCL dataset and colon dataset, ant colony algorithm had the better effect; in prostate dataset, although the classification results were about the same, ant colony algorithm still had faster convergence than genetic algorithm. The improved ant colony algorithm presented a feasible and effective way to solve fuzzy measures in Choquet integral model.

Key words: Choquet fuzzy integral, ant colony algorithm, cancer classification, fuzzy measures, genetic algorithm

中图分类号: 

  • TP399
[1] WANG Z, KLIR G J. Fuzzy measure theory[J]. Springer Berlin, 1992, 35(1-2): 3-10.
[2] SUGENO M. Fuzzy measures and fuzzy integrals:a survey[J]. Readings in Fuzzy Sets for Intelligent Systems, 1993, 6: 251-257.
[3] MUROFUSHI T, SUGENO M. A theory of fuzzy measures: representations, the Choquet integral, and null sets[J]. Journal of Mathematical Analysis & Applications, 1991, 159(2): 532-549.
[4] WANG Z, LEUNG K S, WONG M L, et al. A new type of nonlinear integrals and the computational algorithm[J]. Fuzzy Sets & Systems, 2000, 112(2): 223-231.
[5] LEUNG K S, LEE K H, WANG J F, et al. Data mining on DNA sequences of hepatitis B virus[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2011, 8(2): 428-440.
[6] 冯慧敏, 闫巍, 李雪非. 基于Choquet积分的非线性虫害预测[J]. 湖北农业科学, 2013, 52(22): 5485-5487. FENG Huimin, YAN Wei, LI Xuefei. Non-linear prediction of insects based on Choquet integral[J]. Hubei Agricultural Sciences, 2013, 52(22): 5485-5487.
[7] 秦娟, 李延来, 陈振颂. 基于极大熵配置模型与Choquet积分的物流供应商选择群决策方法[J]. 计算机集成制造系统, 2015, 21(10): 2746-2759. QIN Juan, LI Yanlai, CHEN Zhensong. Group decision making method for supplier selection based on maximum entropy optimization model and Choquet integral[J]. Computer Integrated Manufacturing Systems, 2015, 21(10): 2746-2759.
[8] 王文周, 施黎蒙, 林则夫. 基于 Choquet 积分的绩效评价模型研究:以建筑企业为例[J]. 中国海洋大学学报(社会科学版), 2015(5): 79-85. WANG Wenzhou, SHI Limeng, LIN Zefu. A study on the model of performance evaluation based on Choquet integral: a case study of construction enterprise[J]. Periodical of Ocean University of China(Social Science), 2015(5): 79-85.
[9] WANG Z, LEUNG K S, WONG M L, et al. Nonlinear nonnegative multiregressions based on Choquet integrals[J]. International Journal of Approximate Reasoning, 2000, 25(2): 71-87.
[10] WANG Z, GUO H F. A new genetic algorithm for nonlinear multiregressions based on generalized Choquet integrals[C] //The 12th IEEE International Conference. Missouri, USA: IEEE, 2003:819-821.
[11] YANG R, WANG Z, HENG P A, et al. Fuzzy numbers and fuzzification of the Choquet integral[J]. Fuzzy Sets & Systems, 2005, 153(1): 95-113.
[12] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66.
[13] 焦留成, 邵创创, 程志平. 一种求解连续空间约束优化问题的蚁群算法[J]. 郑州大学学报(工学版), 2015, 36(1): 20-23. JIAO Liucheng, SHAO Chuangchuang, CHENG Zhiping. Ant colony algorithm for solving continuous space constrained optimization problems[J]. Journal of Zhengzhou University(Engineering Science), 2015, 36(1): 20-23.
[14] SHIPP M A, ROSS K N, TAMAYO P, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning[J]. Nature Medicine, 2002, 8(1): 68-74.
[15] SINGH D, FEBBO P G, ROSS K, et al. Gene expression correlates of clinical prostate cancer behavior[J]. Cancer Cell, 2002, 1(2): 203-209.
[16] ALON U, BARKAI N, NOTTERMAN D A, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays[J]. Proceedings of the National Academy of Sciences, 1999, 96(12): 6745.
[17] 高山,欧剑虹,肖凯. R语言与Bioconductor生物信息学应用[M]. 天津: 天津科技翻译出版有限公司, 2014: 106-150.
[18] SMYTH G K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments[J]. Statistical Applications in Genetics and Molecular Biology, 2004, 3(1):1-25.
[19] QUINLAN J R. Induction on decision tree[J]. Machine Learning, 1986, 1(1): 81-106.
[20] CHANG C C, Lin C J. LIBSVM: a library for support vector machines[J]. Acm Transactions on Intelligent Systems & Technology, 2011, 2(3): 27.
[21] COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
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