摘要
针对粗糙集模型中特征选择方法存在计算开销大、不能直接处理连续数据,以及海洋捕食者算法(MPA)处理优化问题仍存在收敛速度慢、易陷入局部最优等问题,提出了基于邻域粗糙集(NRS)和海洋捕食者算法的特征选择方法.首先,使用基于Tent混沌映射的反向学习和高斯扰动策略对原算法改进得到IMPA,再构建一种传输机制形成一种二进制算法;然后,基于邻域依赖度和特征子集长度构造适应度函数,使用IMPA不断迭代搜索出最优特征子集,设计一种元启发式特征选择算法.最后,在9个基准测试函数上评估IMPA的优化性能以及在UCI数据集上评估特征选择算法的分类能力.实验结果表明,在9个基准测试函数上IMPA的平均值、标准差明显优于粒子群优化算法(PSO)和樽海鞘算法(SSA);在UCI数据集上,同基于粗糙集的优化特征选择算法、基于邻域粗糙集的优化特征选择算法相比,所提的特征选择方法在KNN分类器下的分类精度平均值分别提高了10.28~14.13个百分点、2.71~12.11个百分点,在CART分类器下的分类精度平均值分别提高了9.41~13.24个百分点、2.90~12.31个百分点.
he feature selection method in rough set model has large computational overhead and can’t directly handle continuous data,and Marine Predator Algorithm(MPA)still has some problems,such as slower convergence speed and easy to fall into local optimum.Therefore,a feature selection method based on neighborhood rough set(NRS)and Marine Predator Algorithm is proposed.Firstly,the original algorithm is improved by using opposition-based learning based on Tent Chaotic Map and Gaussian Perturbation strategy to obtain IMPA,and then a transmission mechanism is constructed to form a binary algorithm.Then,a fitness function is developed based on the neighborhood dependence in NRS and the length of feature subset.IMPA is used to iteratively search for the optimal feature subset,and a meta-heuristic feature selection algorithm is designed.Finally,the optimization performance of IMPA on 9 benchmark functions and the classification ability of feature selection algorithm on UCI data set is evaluated.Experimental results show IMPA is significantly better than Particle Swarm Optimization(PSO)algorithm and Salp Swarm Algorithm(SSA)in terms of average value and standard deviation.On UCI datasets,compared with the optimized feature selection algorithms based on rough sets and the optimized feature selection algorithms based on neighborhood rough sets,the average values of classification accuracy of the proposed feature selection method using KNN classifier is improved by 10.28~14.13 percentage points,2.71~12.11 percentage points respectively.The average values of classification accuracy of the proposed feature selection method using CART classifier is improved by 9.41~13.24 percentage points,2.90~12.31 percentage points respectively.
作者
龚荣
谢宁新
李德伦
何雪东
GONG Rong;XIE Ningxin;LI Delun;HE Xuedong(School of Artificial Intelligence,Guangxi University for Nationalities,Guangxi 530006,China;College of Electronic Information,Guangxi University for Nationalities,Guangxi 530006,China)
出处
《微电子学与计算机》
2022年第9期35-45,共11页
Microelectronics & Computer
基金
国家自然科学基金(11971420)
广西民族大学软件工程校级重点实验室(2021-18xjsy-03)
广西科技项目(2017AD23056)。
关键词
海洋捕食者算法
邻域粗糙集
邻域依赖度
特征选择
marine predator algorithm(MPA)
neighborhood rough sets
neighborhood dependence
feature selection
作者简介
龚荣,男,(1997-),硕士研究生.研究方向为智能算法与粗糙集;通讯作者:谢宁新,男,(1964-),硕士,教授.研究方向为粗糙集.E-mail:xieningxind1@163.com;李德伦,男,(1997-),硕士研究生.研究方向为机器学习与气象大数据;何雪东,男,(1997-),硕士研究生.研究方向为语义分割.
