[1] Zhang L, Zhang F, Hu Y. A Two-phase Flight Data Feature Selection Method Using both Filter and Wrapper[C]// IEEE International Conference on Systems, Man, and Cybernetics, 1999. IEEE Smc '99 Conference Proceedings. IEEE, 1999:132-136 vol.2.

[2] 姚旭, 王曉丹, 張玉璽,等. 特征選擇方法綜述[J]. 控制與決策, 2012, 27(2):161-166.

By, Yao Xu,Wang Xiaodan, Zhang Yuxi, et. A review of feature selection methods [J]. Control and decision, 2012, 27 (2): 161-166.

[3] Zhang Y, Gong D, Hu Y, et al. Feature selection algorithm based on bare bones particle swarm optimization[J]. Neurocomputing, 2015, 148(1):150-157.

[4] Bolón-Canedo V, Sánchez-Maro?o N, Alonso-Betanzos A. Feature selection for high-dimensional data[J]. Progress in Artificial Intelligence, 2016, 5(2):65-75.

[5] Apolloni J, Alba E. Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments[M]. Elsevier Science Publishers B. V. 2016.

[6] Solorio-Fernández S, Carrasco-Ochoa J A, Martínez-Trinidad J F. A new hybrid filter–wrapper feature selection method for clustering based on ranking[J]. Neurocomputing, 2016, 214:866-880.

[7] 趙榮珍, 李坤杰, 鄧林峰. 基于ReliefF和遺傳算法的故障特征篩選方法[C]// 全國設備監測診斷與維護學術會議、全國設備故障診斷學術會議暨2014年全國設備診斷工程會議. 2014.

Zhao Rongzhen, Li Kunjie, Deng Linfeng. Fault feature screening method based on Relieff and genetic algorithm [c]//National equipment monitoring and diagnosis and maintenance academic conference, national conference on Equipment Fault diagnosis and 2014 National Equipment Diagnostic Engineering Conference. 2014.

[8] 何濤, 胡潔, 夏鵬,等. 基于ReliefF算法與遺傳算法的肌電信號特征選擇[J].上海交通大學學報,2016, 50(2):204-208..

He Tao, Hujie, Xia Peng, et. EMG signal Feature selection based on Relieff algorithm and genetic algorithm [J]. Journal of Shanghai Jiaotong University, 2016, 50 (2): 204-208.

[9] 肖艷, 姜琦剛, 王斌,等. 基于Relief F和PSO混合特征選擇的面向對象土地利用分類[J]. 農業工程學報, 2016, 32(4):211-216.

Xiao Yan, Jiang Qigang, Wang Bin, et al. Object oriented land use classification based on Relief F and PSO hybrid feature selection [J]. Acta agronomy Sinica, 2016, 32 (4): 211-216..

[10] 孫俊. 量子行為粒子群優化算法研究[D]. 江南大學, 2009.

CMa. Research on particle swarm optimization algorithm for quantum behavior [D]. Jiangnan University, 2009.

[11] Yuan Q, Zhou Z, Yang K. Ultrasonic Non-destructiveClassification for Wood Materials Based on ReliefF and PSO-SVM[J]. Journal of Green Science & Technology, 2016.

[12] 霍天龍, 趙榮珍, 胡寶權. 基于熵帶法與PSO優化的SVM轉子故障診斷[J]. 振動.測試與診斷, 2011, 31(3):279-284.

Huo Tianlong, Zhao Rongzhen, Hu Baoquan. SVM Rotor Fault Diagnosis Based on Entropy Band Method and PSO Optimization [J]. Vibration. Testing and Diagnosis, 2011, 31(3): 279-284.

[13] 張欽禮, 王兢, 王新民. 基于核主成分分析與PSO-SVM的充填管道失效風險性分級評價模型[J]. 黃金科學技術, 2017, 25(3):70-76.

Zhang Qinli, Wang Jing, Wang Xinmin. Based on nuclear principal component analysis and PSO-SVM classification evaluation model of filling pipeline failure risk [J]. gold science technology, 2017, 25 (3): 70-76.

[14] 柳一鳴. 自適應量子行為粒子群算法及其在圖像分類中的應用研究[D]. 浙江大學, 2011.

Liu. Adaptive quantum behavior particle swarm optimization and its application in image classification [D]. Zhejiang University, 2011.

[15] 張燕君, 張芳草, 付興虎,等. 基于QPSO-MLSSVM算法的拉曼光譜檢測四組分調和油含量[J]. 光譜學與光譜分析, 2018, 38(5).

Zhang Yanjun, Zhang Cao, Shinghung, et. Determination of four-component blending oil content by Raman spectroscopy based on QPSO-MLSSVM algorithm [J]. Spectroscopy and spectral analysis, 2018, 38 (5).

總結

以上是生活随笔為你收集整理的python特征选择relieff图像特征优选_ReliefF与QPSO结合的故障特征选择算法的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。