1 |
BATTITI R . Using mutual information for selecting features in supervised neural net learning[J]. IEEE Trans.on Neural Networks, 1994, 5 (4): 537- 550.
doi: 10.1109/72.298224
|
2 |
PENG H C , LONG F H , DING H Q . Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2005, 27 (8): 1226- 1238.
doi: 10.1109/TPAMI.2005.159
|
3 |
LI J D , CHENG K W , WANG S H , et al. Feature selection: a data perspective[J]. ACM Computing Surveys, 2017, 50 (6): 94.
|
4 |
李红光, 郭英, 眭萍, 等. 基于高维特征选择的跳频电台细微特征识别[J]. 系统工程与电子技术, 2020, 42 (2): 445- 451.
|
|
LI H G , GUO Y , SUI P , et al. Fine feature recognition of frequency hopping radio based on high dimensional feature selection[J]. Systems Engineering and Electronics, 2020, 42 (2): 445- 451.
|
5 |
WANG J , WEI J M , YANG Z , et al. Feature selection by maximizing independent classification information[J]. IEEE Trans.on Knowledge and Data Engineering, 2017, 29 (4): 828- 841.
doi: 10.1109/TKDE.2017.2650906
|
6 |
GAO W F , HU L , ZHANG P , et al. Feature selection considering the composition of feature relevancy[J]. Pattern Recognition Letters, 2018, 112 (9): 70- 74.
|
7 |
GAO W F , HU L , ZHANG P . Class-specific mutual information variation for feature selection[J]. Pattern Recognition, 2018, 79, 328- 339.
doi: 10.1016/j.patcog.2018.02.020
|
8 |
HOQUE N , BHATTACHARYYA D K , KALITA J K . MIFS-ND: a mutual information-based feature selection method[J]. Expert Systems with Applications, 2014, 41 (14): 6371- 6385.
doi: 10.1016/j.eswa.2014.04.019
|
9 |
ESTEVEZ P A , TESMER M , PEREZ C A , et al. Normalized mutual information feature selection[J]. IEEE Trans.on Neural Networks, 2009, 20 (2): 189- 201.
doi: 10.1109/TNN.2008.2005601
|
10 |
GU X Y , GUO J C , LI C Y , et al. A feature selection algorithm based on redundancy analysis and interaction weight[J]. Applied Intelligence, 2021, 51 (4): 2672- 2686.
doi: 10.1007/s10489-020-01936-5
|
11 |
GONZÁLEZ J , ORTEGA J , DAMAS M , et al. A new multi-objective wrapper method for feature selection-accuracy and stability analysis for BCI[J]. Neuro Computing, 2019, 333, 407- 418.
|
12 |
SHARMA A , RANI R . C-HMOSHSSA: gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods[J]. Computer Methods and Programs in Biomedicine, 2019, 178, 219- 235.
doi: 10.1016/j.cmpb.2019.06.029
|
13 |
KIZILOZ H E , DENIZ A , DOKEROGLU T , et al. Novel multiobjective TLBO algorithms for the feature subset selection problem[J]. Neuro Computing, 2018, 306, 94- 107.
|
14 |
ZHU Y Y , LIANG J W , CHEN J Y , et al. An improved NSGA-Ⅲ algorithm for feature selection used in intrusion detection[J]. Knowledge-Based Systems, 2017, 116, 74- 85.
doi: 10.1016/j.knosys.2016.10.030
|
15 |
KOZODOI N , LESSMANN S , PAPAKONSTANTINOU K , et al. A multi-objective approach for profit-driven feature selection in credit scoring[J]. Decision Support Systems, 2019, 120, 106- 117.
doi: 10.1016/j.dss.2019.03.011
|
16 |
SINGH U , SINGH S N . Optimal feature selection via NSGA-Ⅱ for power quality disturbances classification[J]. IEEE Trans.on Industrial Informatics, 2017, 14 (7): 2994- 3002.
|
17 |
MLAKAR U , FISTER I , BREST J , et al. Multi-objective differential evolution for feature selection in facial expression recognition systems[J]. Expert Systems with Applications, 2017, 89, 129- 137.
doi: 10.1016/j.eswa.2017.07.037
|
18 |
AMOOZEGAR M , MINAEI-BIDGOLI B . Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism[J]. Expert Systems with Applications, 2018, 113 (12): 499- 514.
|
19 |
LABANI M, MORADI P, JALILI M, et al. An evolutionary based multi-objective filter approach for feature selection[C]//Proc. of the World Congress on Computing and Communication Technologies, 2017.
|
20 |
DONG H B , SUN J , LI T , et al. A multi-objective algorithm for multi-label filter feature selection problem[J]. Applied Intelligence, 2020, 50 (11): 3748- 3774.
doi: 10.1007/s10489-020-01785-2
|
21 |
DEB K , PRATAP A , AGARWAL S , et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Trans.on Evolutionary Computation, 2002, 6 (2): 182- 197.
doi: 10.1109/4235.996017
|
22 |
DEB K , JAIN H . An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part Ⅰ: solving problems with box constraints[J]. IEEE Trans.on Evolutionary Computation, 2014, 18 (4): 577- 601.
doi: 10.1109/TEVC.2013.2281535
|
23 |
DONG H B , SUN J , SUN X H , et al. A many-objective feature selection for multi-label classification[J]. Knowledge-Based Systems, 2020, 208 (7): 106456.
|
24 |
TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2011: 4144-4147.
|
25 |
YU Y , YU D J , CHENG J S . A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J]. Journal of Sound and Vibration, 2006, 294 (1-2): 269- 277.
doi: 10.1016/j.jsv.2005.11.002
|
26 |
ZHANG A H, YANG B, HUANG L. Feature extraction of EEG signals using power spectral entropy[C]//Proc. of the International Conference on BioMedical Engineering and Informatics, 2008: 435-439.
|
27 |
李晨, 杨俊安, 刘辉. 基于信息熵和GA-ELM的调制识别算法[J]. 系统工程与电子技术, 2020, 42 (1): 223- 229.
|
|
LI C , YANG J A , LIU H . Modulation recognition algorithm based on information entropy and GA-ELM[J]. Systems Engineering and Electronics, 2020, 42 (1): 223- 229.
|
28 |
ZHENG F , ZHANG G L , SONG Z J . Comparison of different implementations of MFCC[J]. Journal of Computer Science and Technology, 2001, 16 (6): 582- 589.
doi: 10.1007/BF02943243
|