Genetic algorithm-tuned adaptive pruning SVDD method for HRRP-based radar target recognition

ABSTRACT A novel machine learning method named adaptive pruning support vector data description (APSVDD) is developed to classify the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The APSVDD method not only inherits the advantage of least square support vector machine (LSSVM) model, which owns low computational complexity with linear equality constraints so that it is convenient to prune the boundary of SVDD dynamically and rapidly, but also overcomes the shortcoming of ability to deal with outliers in SVDD so that it can enclose targets and exclude outliers simultaneously. Genetic algorithm (GA) tunes the pruning direction of ‘shear’ dynamically, reducing the empirical risk. And fuzzy membership contributes to decision of classes for multiclass fuzzy areas. Besides, similar to the LSSVM, the distribution information within classes is found by least square method and applied for adjusting the pruning depth of ‘shear’ in APSVDD. Hence, there will be a remarkable improvement in recognition accuracy and generalization performance. Numerical experiments based on two publicly UCI datasets and remotely sensed data of four aircrafts can demonstrate the feasibility, repeatability and superiority of the proposed method. The APSVDD is ideal for HRRP-based radar target recognition.

[1]  Divya Tomar,et al.  A comparison on multi-class classification methods based on least squares twin support vector machine , 2015, Knowl. Based Syst..

[2]  Richard Weber,et al.  Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines , 2014, Inf. Sci..

[3]  Bartosz Krawczyk,et al.  Clustering-based ensembles for one-class classification , 2014, Inf. Sci..

[4]  Xueying Zhang,et al.  Robust support vector data description for outlier detection with noise or uncertain data , 2015, Knowl. Based Syst..

[5]  Zheng Bao,et al.  Noise Robust Radar HRRP Target Recognition Based on Multitask Factor Analysis With Small Training Data Size , 2012, IEEE Transactions on Signal Processing.

[6]  Le Zhao,et al.  Noise Robust Radar HRRP Target Recognition Based on Scatterer Matching Algorithm , 2016, IEEE Sensors Journal.

[7]  Mac McKee,et al.  Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines , 2012 .

[8]  Yang Zhang,et al.  Traffic forecasting using least squares support vector machines , 2009 .

[9]  Yuan Zhang,et al.  Support vector machine approach to identifying buildings using multi-temporal ALOS/PALSAR data , 2011 .

[10]  Hongwei Liu,et al.  Radar high-resolution range profiles target recognition based on stable dictionary learning , 2016 .

[11]  Huafu Chen,et al.  Two-class support vector data description , 2011, Pattern Recognit..

[12]  Zheng Bao,et al.  Noise-Robust Modification Method for Gaussian-Based Models With Application to Radar HRRP Recognition , 2013, IEEE Geosci. Remote. Sens. Lett..

[13]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[14]  Alfredo Petrosino,et al.  Adjusted F-measure and kernel scaling for imbalanced data learning , 2014, Inf. Sci..

[15]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[16]  Yu Guo,et al.  Least square support vector data description for HRRP-based radar target recognition , 2016, Applied Intelligence.

[17]  Seetha Hari,et al.  Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.

[18]  Francisco Herrera,et al.  On the usefulness of one-class classifier ensembles for decomposition of multi-class problems , 2015, Pattern Recognit..

[19]  Fan Guo,et al.  Genetic algorithm-based parameter selection approach to single image defogging , 2016, Inf. Process. Lett..

[20]  J.-S. Fu,et al.  KFD-Based Multiclass Synthetical Discriminant Analysis for Radar HRRP Recognition , 2012 .

[21]  Tingting Mu,et al.  Multiclass Classification Based on Extended Support Vector Data Description , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Gerald Schaefer,et al.  Cost-sensitive decision tree ensembles for effective imbalanced classification , 2014, Appl. Soft Comput..

[23]  Luís Torgo,et al.  A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..

[24]  Ning Fang,et al.  Multi-scale feature-based fuzzy-support vector machine classification using radar range profiles , 2016 .

[25]  Bartosz Krawczyk,et al.  Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets , 2016, Pattern Recognit..

[26]  Le Wang,et al.  Develop an Ensemble Support Vector Data Description method for improving invasive tamarisk mapping at regional scale , 2014 .

[27]  Yi-Hung Liu,et al.  Fast Support Vector Data Descriptions for Novelty Detection , 2010, IEEE Transactions on Neural Networks.

[28]  Ethem Alpaydın,et al.  Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms , 1999, Neural Comput..

[29]  Taeho Jo,et al.  A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..

[30]  Robert Jenssen,et al.  Land-cover classification of partly missing data using support vector machines , 2012 .

[31]  Luís Torgo,et al.  A Survey of Predictive Modelling under Imbalanced Distributions , 2015, ArXiv.

[32]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[33]  J.S.H. Tsai,et al.  A boundary method for outlier detection based on support vector domain description , 2009, Pattern Recognit..

[34]  Changshui Zhang,et al.  Solving one-class problem with outlier examples by SVM , 2015, Neurocomputing.

[35]  Yijing Li,et al.  Learning from class-imbalanced data: Review of methods and applications , 2017, Expert Syst. Appl..

[36]  Zheng Bao,et al.  Noise-Robust Modification Method for Gaussian-Based Models With Application to Radar HRRP Recognition , 2013, IEEE Geoscience and Remote Sensing Letters.