[1] 张翠军, 陈贝贝, 周冲, 等. 基于多目标骨架粒子群优化的特征选择算法[J]. 计算机应用,2018,38(11):3156-3160,3166. (ZHANG C J,CHEN B B,ZHOU C,et al. Feature selection algorithm based on multi-objective bare-bones particle swarm optimization[J]. Journal of Computer Applications, 2018, 38(11):3156-3160,3166.) [2] 谢琪, 徐旭, 程耕国, 等. 基于新的森林优化算法的特征选择算法[J]. 计算机应用,2020,40(5):1266-1271.(XIE Q,XU X, CHENG G G,et al. Feature selection algorithm based on new forest optimization algorithm[J]. Journal of Computer Applications, 2020,40(5):1266-1271.) [3] 谢娟英, 王明钊, 周颖, 等. 非平衡基因数据的差异表达基因选择算法研究[J]. 计算机学报,2019,42(6):1232-1251.(XE J Y, WANG M Z,ZHOU Y,et al. Differential expression gene selection algorithms for unbalanced gene datasets[J]. Chinese Journal of Computers,2019,42(6):1232-1251.) [4] 王翔, 胡学钢. 高维小样本分类问题中特征选择研究综述[J]. 计算机应用,2017,37(9):2433-2438,2448.(WANG X,HU X G. Overview on feature selection in high-dimensional and smallsample-size classification[J]. Journal of Computer Applications, 2017,37(9):2433-2438,2448.) [5] 樊鑫, 陈红梅. 基于差别矩阵和mRMR的分步优化特征选择算法[J]. 计算机科学,2020,47(1):87-95.(FAN X,CHEN H M. Stepwise optimized feature selection algorithm based on discernibility matrix and mRMR[J]. Computer Science,2020,47(1):87-95.) [6] 姚登举. 面向医学数据的随机森林特征选择及分类方法研究[D]. 哈尔滨:哈尔滨工程大学,2016:1-2.(YAO D J. Research on feature selection and classification method based on random forest for medical datasets[D]. Harbin:Harbin Engineering University,2016:1-2.) [7] 李占山, 刘兆赓. 基于XGBoost的特征选择算法[J]. 通信学报, 2019,40(10):101-108.(LI Z S,LIU Z G. Feature selection algorithm based on XGBoost[J]. Journal on Communications, 2019,40(10):101-108.) [8] CHANDRASHEKAR G,SAHIN F. A survey on feature selection methods[J]. Computers and Electrical Engineering,2014,40(1):16-28. [9] SHARMIN S,SHOYAIB M,ALI A A,et al. Simultaneous feature selection and discretization based on mutual information[J]. Pattern Recognition,2019,91:162-174. [10] 谢娟英, 谢维信. 基于特征子集区分度与支持向量机的特征选择算法[J]. 计算机学报,2014,37(8):1704-1718.(XIE J Y, XIE W X. Several feature selection algorithms based on the discernibility of a feature subset and support vector machines[J]. Chinese Journal of Computers,2014,37(8):1704-1718.) [11] 张鑫. 基于自然进化策略的特征选择算法研究[D]. 长春:吉林大学,2020:1-2. (ZHANG X. Research of feature selection algorithm based on natural evolution strategy[D]. Changchun:Jilin University,2020:1-2.) [12] 李航. 统计学习方法[M]. 北京:清华大学出版社,2012:72-75.(LI H. Statistical Learning Methods[M]. Beijing:Tsinghua University Press,2012:72-75.) [13] MOUSTAKIDIS S P,THEOCHARIS J B. SVM-FuzCoC:a novel SVM-based feature selection method using a fuzzy complementary criterion[J]. Pattern Recognition,2010,43(11):3712-3729. [14] SUN Z Q,ZHANG J,DAI L,et al. Mutual information based multi-label feature selection via constrained convex optimization[J]. Neurocomputing,2019,329:447-456. [15] SHEIKHI G,ALTINÇAY H. maximum-relevance and maximumdiversity of positive ranks:a novel feature selection method[J]. Expert Systems with Applications,2020,158:No. 113499. [16] GHAEMI M,FEIZI-DERAKHSHI M R. Feature selection using forest optimization algorithm[J]. Pattern Recognition,2016,60:121-129. [17] 张梦林, 李占山. 基于SAC的特征选择算法[J]. 计算机科学, 2018,45(2):63-68.(ZHANG M L,LI Z S. Feature selection algorithm using SAC algorithm[J]. Computer Science,2018,45(2):63-68.) [18] 李光华, 李俊清, 张亮, 等. 一种融合蚁群算法和随机森林的特征选择方法[J]. 计算机科学,2019,46(11A):212-215.(LI G H,LI J Q,ZHANG L,et al. Feature selection method based on ant colony optimization and random forest[J]. Computer Science, 2019,46(11A):212-215.) [19] TABAKHI S,MORADI P,AKHLAGHIAN F. An unsupervised feature selection algorithm based on ant colony optimization[J]. Engineering Applications of Artificial Intelligence,2014,32:112-123. [20] MA J B,GAO X Y. Designing genetic programming classifiers with feature selection and feature construction[J]. Applied Soft Computing,2020,97(Pt B):No. 106826. [21] MA J B,GAO X Y. A filter-based feature construction and feature selection approach for classification using Genetic Programming[J]. Knowledge-Based Systems,2020,196:No. 105806. [22] FERREIRA C. Gene expression programming:a new adaptive algorithm for solving problems[J]. Complex Systems,2001,13(2):87-129. [23] 崔未. 基于层次距离的GEP算法及其应用[D]. 武汉:武汉理工大学,2019:17-28.(CUI W. Application of gene expression programming based on layer distance[D]. Wuhan:Wuhan University of Technology,2019:17-28.) [24] DUA D,GRAFF C. UCI machine learning repository[DS/OL].[2020-11-11]. http://archive.ics.uci.edu/ml. [25] ZHU W Z,SI G Q,ZHANG Y B,et al. Neighborhood effective information ratio for hybrid feature subset evaluation and selection[J]. Neurocomputing,2013,99:25-37. [26] HU Q H,CHE X J,ZHANG L,et al. Feature evaluation and selection based on neighborhood soft margin[J]. Neurocomputing, 2010,73(10/11/12):2114-2124. [27] XUE B, ZHANG M J, BROWNE W N. Particle swarm optimisation for feature selection in classification:novel initialisation and updating mechanisms[J]. Applied Soft Computing,2014,18:261-276. [28] HUANG J J,CAI Y Z,XU X M. A hybrid genetic algorithm for feature selection wrapper based on mutual information[J]. Pattern Recognition Letters,2007,28(13):1825-1844. [29] ZHOU H F,WEN J. Dynamic feature selection method with minimum redundancy information for linear data[J]. Applied Intelligence,2020,50(11):3660-3677. [30] GAO W F,HU L,ZHANG P. Class-specific mutual information variation for feature selection[J]. Pattern Recognition,2018,79:328-339. [31] HU Q H,ZHANG L,CHEN D G,et al. Gaussian kernel based fuzzy rough sets:model,uncertainty measures and applications[J]. International Journal of Approximate Reasoning,2010,51(4):453-471. [32] HU Q H,YU D R,LIU J F,et al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information Sciences, 2008,178(18):3577-3594. [33] HU Q H,PEDRYCZ W,YU D R,et al. Selecting discrete and continuous features based on neighborhood decision error minimization[J]. IEEE Transactions on Systems, Man, and Cybernetics,Part B(Cybernetics),2010,40(1):137-150. [34] 王颖, 曹捷, 邱志洋. 基于乌鸦搜索算法的新型特征选择算法[J]. 吉林大学学报(理学版),2019,57(4):869-874.(WANG Y,CAO J,QIU Z Y. A novel feature selection algorithm based on crow search algorithm[J]. Journal of Jilin University (Science Edition),2019,57(4):869-874.) [35] 邓秀勤, 李文洲, 武继刚, 等. 融合Shapley值和粒子群优化算法的混合特征选择算法[J]. 计算机应用,2018,38(5):1245-1249.(DENG X Q,LI W Z,WU J G,et al. Hybrid feature selection algorithm fused Shapley value and particle swarm optimization[J]. Journal of Computer Applications,2018,38(5):1245-1249.) [36] SASIKALA S,APPAVU ALIAS BALAMURUGAN S,GEETHA S. A novel adaptive feature selector for supervised classification[J]. Information Processing Letters,2016,117:25-34. |