scholarly journals A two-stage classification algorithm for radar targets based on compressive detection

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Cong Liu ◽  
Yunqing Liu ◽  
Qiong Zhang ◽  
Xiaolong Li ◽  
Tong Wu ◽  
...  
Keyword(s):  
Data Storage ◽  
Support Vector ◽  
False Alarms ◽  
Support Vector Data Description ◽  
Echo Signal ◽  
Two Stage ◽  
First Order ◽  
Stage Classification ◽  
Radar Targets ◽  
One Class Classifier

AbstractAlgorithms are proposed to address the radar target detection problem of compressed sensing (CS) under the conditions of a low signal-to-noise ratio (SNR) and a low signal-to-clutter ratio (SCR) echo signal. The algorithms include a two-stage classification for radar targets based on compressive detection (CD) without signal reconstruction and a support vector data description (SVDD) one-class classifier. First, we present the sparsity of the echo signal in the distance dimension to design a measurement matrix for CD of the echo signal. Constant false alarm rate (CFAR) detection is performed directly on the CD echo signal to complete the first-order target classification. In simulations, the detection performance is similar to that of the traditional matched filtering algorithm, but the data rate is lower, and the necessary data storage space is reduced. Then, the power spectrum features are extracted from the data after the first-order classification and converted to the feature domain. The SVDD one-class classifier is introduced to train and classify the characteristic signals to complete the separation of the targets and the false alarms. Finally, the performance of the algorithm is verified by simulation. The number of false alarms is reduced, and the detection probability of the targets is improved.

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021 ◽  
Vol 10 (2) ◽  
pp. 736-745
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Support Vector ◽  
Two Stage ◽  
Machine Learning Classification ◽  
Second Stage ◽  
Testing Data ◽  
Stage Classification ◽  
Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

scholarly journals A two-stage classification method for borehole-wall images with support vector machine

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0199749
Author(s):  
Zhaopeng Deng ◽  
Maoyong Cao ◽  
Laxmisha Rai ◽  
Wei Gao
Keyword(s):  
Support Vector Machine ◽  
Borehole Wall ◽  
Classification Method ◽  
Support Vector ◽  
Two Stage ◽  
Stage Classification

scholarly journals On-line Signature Verification Based on GA-SVM

2015 ◽  
Vol 11 (6) ◽  
pp. 49 ◽  
Author(s):  
Dong Huang ◽  
Jian Gao
Keyword(s):  
Genetic Algorithm ◽  
Feature Subset Selection ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Support Vector Data Description ◽  
Feature Subset ◽  
Dynamic Features ◽  
On Line ◽  
One Class Classifier

With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.

A pruned support vector data description-based outlier detection method: Applied to robust process monitoring

2020 ◽  
Vol 42 (11) ◽  
pp. 2113-2126 ◽  
Author(s):  
Ping Yuan ◽  
Zhizhong Mao ◽  
Biao Wang
Keyword(s):  
Process Monitoring ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Vector Data ◽  
Training Set ◽  
Data Set ◽  
Data Description ◽  
One Class Classifier ◽  
Comparative Results

Support vector data description (SVDD) is a boundary-based one-class classifier that has been widely used for process monitoring during recent years. However, in some applications where databases are often contaminated by outliers, the performance of SVDD would become deteriorated, leading to low detection rate. To this end, this paper proposes a pruned SVDD model in order to improve its robustness. In contrast to other robust SVDD models that are developed from the algorithmic level, we prune the basic SVDD from a data level. The rationale is to exclude outlier examples from the final training set as many as possible. Specifically, three different SVDD models are constructed successively with different training sets. The first model is used to extract target points by means of rejecting more suspect outlier examples. The second model is constructed using those extracted target points, and is used to recover some false outlier examples labeled by the first model. We build the third (final) model with the final training set consisting of target examples by the first model and false outlier examples by the second model. We validate our proposed method on 20 benchmark data sets and TE data set. Comparative results show that our pruned model could improve the robustness of SVDD more efficiently.

scholarly journals A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Nur Diyana Kamarudin ◽  
Chia Yee Ooi ◽  
Tadaaki Kawanabe ◽  
Hiroshi Odaguchi ◽  
Fuminori Kobayashi
Keyword(s):  
Red Light ◽  
Support Vector ◽  
Computer Assisted ◽  
Transitional Region ◽  
Two Stage ◽  
True Rate ◽  
Lighting Condition ◽  
Tongue Diagnosis ◽  
Support Vectors ◽  
Stage Classification

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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