摘要
针对DNA微阵列的高维、小样本及高冗余等特点,提出了一种新的集成分类方法.基于bootstrap技术的样本扰动和kruskalwallis与邻域互信息的特征扰动训练多个具有较大差异性和较高准确性的基分类器;针对教与学优化算法易陷入局部最优、优化精度不高和收敛速度较慢等不足,从"教"与"自学"过程入手,设计了一种改进的教与学优化算法实现基分类器的选择性集成,并用于DNA微阵列分类.仿真实验结果表明:该方法在分类精度、集成规模、稳定性等方面具有较强的优势.
In view of the characteristics of high dimensional,small sample and high redundancy,an ensemble method for classifying microarray data is proposed.The sample disturbance based on bootstrap and feature disturbance based on kruskalwallis and neighborhood mutual information are used to train multiple base classifiers in order to improve the diversity among the base classifiers and individual precision.An improved TLBO is designed to realize the selective ensemble from two aspects of the"teaching"and"self-learning"process.Simulation results show that the proposed method has strong advantages in terms of classification accuracy,ensemble size and stability.
作者
陈涛
Chen Tao(School of Mathematics and Computer Science,Shaanxi University of Technology,Hanzhong 723000,China)
出处
《中南民族大学学报(自然科学版)》
CAS
2018年第3期99-106,139,共9页
Journal of South-Central University for Nationalities:Natural Science Edition
基金
国家自然科学基金资助项目(11502132)
陕西省教育厅科研资助项目(16JK1149)
陕西理工大学科研资助项目(SLGQD2017-07)
关键词
DNA微阵列
选择性集成
邻域互信息
教与学优化算法
DNA microarray
selective ensemble
neighborhood mutual information
teaching-learning-based optimization