N. Zhang, Jiang Xiong, Jing Zhong, Lara A. Thompson
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引用次数: 6
摘要
提出了一种基于二元粒子群优化(BPSO)和进化算法(EA)的混合特征选择方法。受二进制粒子群的概念启发,在二进制搜索空间中设计了粒子的位置更新过程。适应度函数定义为ENN分类器的准确率。提出并描述了基于混合BPSO-EA学习算法的特征选择方法。实验包括使用和不使用BPSO-EA特征选择方法对新神经网络分类精度的比较。比较了BPSO- ea - enn方法与BPSO+C4.5方法的特征约简率。此外,还将BPSO-EA-ENN与其他分类方法进行了比较。实验结果表明,提出的BPSO-EA特征选择方法提高了分类精度。此外,本文提出的方法在电离层数据集上比BPSO+C4.5特征选择方法具有更高的改进精度和特征约简率,在天秤座运动数据集上比BPSO+C4.5方法具有更高的准确率。此外,我们提出的BPSO-EA-ENN在8个UCI数据集上的总体分类精度优于ENN、KNN、Naïve贝叶斯和LDA分类方法。
Feature Selection Method Using BPSO-EA with ENN Classifier
This paper develops a hybrid binary particle swarm optimization (BPSO) and evolutionary algorithm (EA) based feature selection method. Inspired by the concept of binary PSO, the particle's position updating process is designed in a binary search space. The fitness function is defined as the accuracy of the ENN classifier. The feature selection method using a hybrid BPSO-EA learning algorithm is developed and described. The experiments include the comparison of ENN classification accuracy with and without the BPSO-EA feature selection method. The feature reduction rate between the proposed BPSO-EA-ENN method and the BPSO+C4.5 method is also compared. In addition, a comparison of BPSO-EA-ENN to other classification methods is provided. The experimental results demonstrate that the proposed BPSO-EA feature selection method improves the classification accuracy. In addition, our proposed method has higher improved accuracy and feature reduction rate than the BPSO+C4.5 feature selection method on the Ionosphere data set, as well as better accuracy rate than the BPSO+C4.5 method on the Movement Libra data set. Further, the overall classification accuracy of our proposed BPSO-EA-ENN outperforms ENN, KNN, Naïve Bayes, and LDA classification methods on the eight UCI data sets.