基于已选特征动态变化的非线性特征选择方法

doi:10.13229/j.cnki.jdxbgxb20180478

摘要/Abstract

摘要：

针对目前大多数基于信息论的线性累加特征选择算法的缺点和不足，并且考虑到已选特征的动态变化对特征选择的影响，本文提出了一种非线性的特征选择算法。实验结果证明，本文算法在平均准确率和最高准确率上都取得了很好的效果。为证明本文算法的优势，将本文算法与7个极具竞争性的特征选择算法在3个不同的分类器和8个真实数据集上进行了比较，实验结果表明，本文算法具有较强的分类优势。

关键词: 人工智能, 特征选择, 信息论, 动态变化, 非线性方法, 分类

Abstract:

Information theory is widely used in feature selection methods. Traditional feature selection methods employ cumulative summation to select features. Different from previous feature selection methods, this paper proposes a nonlinear feature selection method that also considers dynamic change of selected features. The experimental results demonstrate that the proposed method achieves the best classification performance in terms of average classification accuracy and highest classification accuracy. To verify the effectiveness of the proposed method, the proposed method is compared with seven very competitive feature selection methods on three different classifiers on eight real-world data sets.The experimental results shows that this algorithm has strong classification superiorities.

Key words: artificial intelligence, feature selection, information theory, dynamic change, nonlinear methods, classification

中图分类号:

TP301

高万夫,张平,胡亮. 基于已选特征动态变化的非线性特征选择方法[J]. 吉林大学学报(工学版), 2019, 49(4): 1293-1300.

Wan-fu GAO,Ping ZHANG,Liang HU. Nonlinear feature selection method based on dynamic change of selected features[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1293-1300.

图/表 13

表1

表2

表3

表4

图1

图2

图3

图4

图5

图6

图7

图8

表5

参考文献 16

1	Gao W , Hu L , Zhang P . Class-specific mutual information variation for feature selection[J]. Pattern Recognition, 2018, 79:328-339.
2	Hu L , Gao W , Zhao K , et al . Feature selection considering two types of feature relevancy and feature interdependency[J]. Expert Systems with Applications, 2018, 93:423-434.
3	石峰，莫忠息 .信息论基础[M].3版.武汉:武汉大学出版社，2014:14-52.
4	赵晓群 .信息论基础及应用[M].北京:机械工业出版社，2015:27-53.
5	Lewis D D . Feature selection and feature extraction for text categorization[C]⫽The Workshop on Speech & Natural Language, Association for Computational Linguistics,Harriman, New York, 1992:212-217.
6	Battiti R . Using mutual information for selecting features in supervised neural net learning[J]. IEEE Transactions on Neural Networks, 1994, 5(4):537-550.
7	Peng H , Long F , Ding C . Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(8):1226-1238.
8	Lin D , Tang X . Conditional infomax learning: an integrated framework for feature extraction and fusion[C]⫽European Conference on Computer Vision, Graz, 2006: 68-82.
9	Salueña C , Avalos J B . Information-theoretic feature selection in microarray data using variable complementarity[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(3):261-274.
10	Zeng Z , Zhang H , Zhang R , et al . A novel feature selection method considering feature interaction[J]. Pattern Recognition, 2015, 48(8):2656-2666.
11	Fleuret F . Fast Binary Feature selection with conditional mutual information[J]. Journal of Machine Learning Research, 2004, 5(3):1531-1555.
12	Bennasar M , Hicks Y , Setchi R . Feature selection using joint mutual information maximisation[J]. Expert Systems with Applications, 2015, 42(22):8520-8532.
13	Wang J , Wei J M , Yang Z , et al . Feature selection by maximizing independent classification information[J]. IEEE Transactions on Knowledge & Data Engineering, 2017, 29(4):828-841.
14	Che J , Yang Y , Li L , et al . Maximum relevance minimum common redundancy feature selection for nonlinear data[J]. Information Sciences, 2017, 409:68-86.
15	Li J , Cheng K , Wang S , et al . Feature selection: a data perspective[J]. Acm Computing Surveys, 2016, 50(6).
16	Lichman M . UCI machine learning repository[DB/OL].[2018-05-15]..

相关文章 15

[1]	欧阳丹彤,肖君,叶育鑫. 基于实体对弱约束的远监督关系抽取[J]. 吉林大学学报(工学版), 2019, 49(3): 912-919.
[2]	董飒, 刘大有, 欧阳若川, 朱允刚, 李丽娜. 引入二阶马尔可夫假设的逻辑回归异质性网络分类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1571-1577.
[3]	顾海军, 田雅倩, 崔莹. 基于行为语言的智能交互代理[J]. 吉林大学学报(工学版), 2018, 48(5): 1578-1585.
[4]	王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[5]	张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[6]	李雄飞, 冯婷婷, 骆实, 张小利. 基于递归神经网络的自动作曲算法[J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7]	黄岚, 纪林影, 姚刚, 翟睿峰, 白天. 面向误诊提示的疾病-症状语义网构建[J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[8]	刘杰, 张平, 高万夫. 基于条件相关的特征选择方法[J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[9]	陈涛, 崔岳寒, 郭立民. 适用于单快拍的多重信号分类改进算法[J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[10]	陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[11]	刘雪娟, 袁家斌, 许娟, 段博佳. 量子k-means算法[J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[12]	王旭, 欧阳继红, 陈桂芬. 基于多重序列所有公共子序列的启发式算法度量多图的相似度[J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[13]	杨欣, 夏斯军, 刘冬雪, 费树岷, 胡银记. 跟踪-学习-检测框架下改进加速梯度的目标跟踪[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[14]	杨宏宇, 徐晋. Android恶意软件静态检测模型[J]. 吉林大学学报(工学版), 2018, 48(2): 564-570.
[15]	范敏, 韩琪, 王芬, 宿晓岚, 徐浩, 吴松麟. 基于多层次特征表示的场景图像分类算法[J]. 吉林大学学报(工学版), 2017, 47(6): 1909-1917.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

数据集	样本个数	特征数目	类别数目	类型
Semeion	1593	256	10	离散
Movement_libras	360	90	15	连续
WarpPIE10P	210	2420	10	连续
TOX_171	171	5748	4	连续
SMK_CAN_187	187	19993	2	连续
Isolet	1560	617	26	连续
USPS	9298	256	10	连续
RELATHE	1427	4322	2	离散

Data sets	CIFE	CMIM	DISR	mRMR	IWFS	MRI	JMIM	NDCSF
Semeion	64.81	75.75	63.28	65.20	63.47	67.53	69.60	78.01
Movement_libras	66.33	71.89	61.89	69.33	67.81	71.85	71.52	72.89
WarpPIE10P	80.33	89.78	80.06	85.11	85.67	83.17	87.06	93.39
TOX_171	59.93	68.15	60.54	59.69	57.63	62.86	59.64	63.98
SMK_CAN_187	66.04	64.61	65.94	65.11	65.36	65.34	64.61	71.32
Isolet	65.24	75.90	64.53	64.96	60.04	73.82	63.35	76.39
USPS	84.46	90.54	83.63	85.96	84.23	85.79	87.46	91.65
RELATHE	66.08	72.76	70.24	73.58	68.91	69.42	70.22	75.46
Average	69.15	76.17	68.76	71.12	69.14	72.47	71.68	77.89