基于混合ABC和CRO的高维特征选择方法
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摘要
高维数据集包含了成千上万可用于数据分析和预测的特征,然而这些数据集存在许多不相关或冗余特征,影响了数据分析和预测的准确性。现有分类技术难以准确地识别最佳特征子集。针对该问题,提出了一种基于wrapper模式的特征选择方法 AB-CRO,该方法结合了人工蜂群算法(ABC)和改进的化学反应算法(CRO)的优点进行特征选择。针对迭代过程中较优的个体可能在化学反应过程中被消耗掉的现象,适当地加入精英策略来保持种群的优良性。实验结果表明,AB-CRO算法在最佳特征子集的识别和分类精度方面相对于基准算法ABC,CRO以及基于GA,PSO和混合蛙跳算法都所有改进。
The high-dimensional data set contains thousands of features that can be used for data analyzing and forecasting. However, these data sets have many unrelated or redundant features that affect the accuracy of data analyzing and forecasting. Existing classification techniques are difficult to accurately identify the best subset of features. Aiming at this problem, this paper proposes a feature selection method AB-CRO based on wrapper mode, which combines the advantages of Artificial Bee Colony algorithm(ABC)and improved Chemical Reaction algorithm(CRO)to select features. In view of the phenomenon that the superior individuals in the iterative process may be consumed during the chemical reaction process, an elite strategy is appropriately added to maintain the superiority of the population. It presents a proposed method comparing the benchmark algorithms ABC, CRO, and biometric selection methods based on GA, PSO, and ISFLA on public data sets. The experimental results show that the proposed algorithm improves in the recognition and classification accuracy of related subsets.
The high-dimensional data set contains thousands of features that can be used for data analyzing and forecasting. However, these data sets have many unrelated or redundant features that affect the accuracy of data analyzing and forecasting. Existing classification techniques are difficult to accurately identify the best subset of features. Aiming at this problem, this paper proposes a feature selection method AB-CRO based on wrapper mode, which combines the advantages of Artificial Bee Colony algorithm(ABC)and improved Chemical Reaction algorithm(CRO)to select features. In view of the phenomenon that the superior individuals in the iterative process may be consumed during the chemical reaction process, an elite strategy is appropriately added to maintain the superiority of the population. It presents a proposed method comparing the benchmark algorithms ABC, CRO, and biometric selection methods based on GA, PSO, and ISFLA on public data sets. The experimental results show that the proposed algorithm improves in the recognition and classification accuracy of related subsets.
引文
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[3] Xue B,Zhang M,Browne W N,et al.A survey on evolutionary computation approaches to feature selection[J].IEEE Transactions on Evolutionary Computation,2016,20(4):606-626.
[4] Wade B S C,Joshi S H,Gutman B A,et al.Machine learning on high dimensional shape data from subcortical brain surfaces:a comparison of feature selection and classification methods[M]//Machine learning in medical imaging.[S.l.]:Springer International Publishing,2015:36-43.
[5]吴争,董育宁.网络视频流量分类的特征选择方法研究[J].计算机工程与应用,2018,54(6):7-13.
[6] Baldi P,Long A D.A Bayesian framework for the analysis of microarray expression data:regularized t-test and statistical inferences of gene changes[J].Bioinformatics,2001,17(2):132-148.
[7]郎茂祥.装卸混合车辆路径问题的模拟退火算法研究[J].系统工程学报,2005,20(5):485-491.
[8]刘小虎,李生.决策树的优化算法[J].软件学报,1998,9(10):797-800.
[9] Verbiest N,Derrac J,Cornelis C,et al.Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines:experimental evaluation and support vector analysis[J].Applied Soft Computing,2016,38(C):10-22.
[10] Vieira S M,Mendon?a L F,Farinha G J,et al.Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients[J].Applied Soft Computing,2013,13(8):3494-3504.
[11] Subanya B,Rajalaxmi R R.A novel feature selection algorithm for heart disease classification[J].International Journal of Computational Intelligence and Informatics,2014,4(2).
[12] Hu B,Dai Y,Su Y,et al.Feature selection for optimized high-dimensional biomedical data using the improved shuf-fled frog leaping algorithm[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2016.
[13] Babatunde O,Armstrong L,Leng J,et al.A genetic algorithm-based feature selection[J].British Journal of Mathematics&Computer Science,2014,5(4):889-905.
[14]刘庆和,梁正友.一种基于信息增益的特征优化选择方法[J].计算机工程与应用,2011,47(12):130-132.
[15] Li Q,Chen H,Huang H,et al.An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis[J].Computational and Mathematical Methods in Medicine,2017.
[16] Karaboga D.An idea based on honey bee swarm for numerical optimization,technical report-tr06[R].Erciyes University.Engineering Faculty.Computer Engineering Department,2005.
[17] Lam A Y S,Li V O K.Chemical-reaction-inspired metaheuristic for optimization[J].IEEE Transactions on Evolutionary Computation,2010,14(3):381-399.
[18]黄珊,高兴宝.具有学习及十字交叉搜索的人工蜂群算法[J].计算机科学与探索,2017,11(12):2004-2014.
[19]朱焕雄,刘波.基于人工蜂群智能技术的属性异常点检测[J].计算机科学与探索,2017,11(12):1984-1992.
[20]林诗洁,董晨,陈明志,等.新型群智能优化算法综述[J].计算机工程与应用,2018,54(12):1-9.
[21]魏锋涛,岳明娟,郑建明.基于多策略融合的改进人工蜂群算法[J].计算机工程与应用,2018,54(5):111-116.
[22] Franek L,Jiang X.Orthogonal design of experiments for parameter learning in image segmentation[J].Signal Processing,2013,93(6):1694-1704.