A hybrid of fast K-nearest neighbor and improved directed acyclic graph support vector machine for large-scale supersonic inlet flow pattern recognition

2021 ◽  
pp. 095441002110086
Author(s):  
Huan Wu ◽  
Yong-Ping Zhao ◽  
Tan Hui-Jun
Keyword(s):  
Pattern Recognition ◽  
Flow Pattern ◽  
Directed Acyclic Graph ◽  
Large Scale ◽  
Computational Cost ◽  
Classification Error ◽  
Support Vector ◽  
Training Set ◽  
Inlet Flow ◽  
Supersonic Inlet

Inlet flow pattern recognition is one of the most crucial issues and also the foundation of protection control for supersonic air-breathing propulsion systems. This article proposes a hybrid algorithm of fast K-nearest neighbors (F-KNN) and improved directed acyclic graph support vector machine (I-DAGSVM) to solve this issue based on a large amount of experimental data. The basic idea behind the proposed algorithm is combining F-KNN and I-DAGSVM together to reduce the classification error and computational cost when dealing with big data. The proposed algorithm first finds a small set of nearest samples from the training set quickly by F-KNN and then trains a local I-DAGSVM classifier based on these nearest samples. Compared with standard KNN which needs to compare each test sample with the entire training set, F-KNN uses an efficient index-based strategy to quickly find nearest samples, but there also exists misclassification when the number of nearest samples belonging to different classes is the same. To cope with this, I-DAGSVM is adopted, and its tree structure is improved by a measure of class separability to overcome the sequential randomization in classifier generation and to reduce the classification error. In addition, the proposed algorithm compensates for the expensive computational cost of I-DAGSVM because it only needs to train a local classifier based on a small number of samples found by F-KNN instead of all training samples. With all these strategies, the proposed algorithm combines the advantages of both F-KNN and I-DAGSVM and can be applied to the issue of large-scale supersonic inlet flow pattern recognition. The experimental results demonstrate the effectiveness of the proposed algorithm in terms of classification accuracy and test time.

scholarly journals A new parallel data geometry analysis algorithm to select training data for support vector machine

2021 ◽  
Vol 6 (12) ◽  
pp. 13931-13953
Author(s):  
Yunfeng Shi ◽  
◽  
Shu Lv ◽  
Kaibo Shi ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Computational Cost ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Redundant Data ◽  
Parallel Data ◽  
Low Efficiency ◽  
Geometry Analysis

<abstract><p>Support vector machine (SVM) is one of the most powerful technologies of machine learning, which has been widely concerned because of its remarkable performance. However, when dealing with the classification problem of large-scale datasets, the high complexity of SVM model leads to low efficiency and become impractical. Due to the sparsity of SVM in the sample space, this paper presents a new parallel data geometry analysis (PDGA) algorithm to reduce the training set of SVM, which helps to improve the efficiency of SVM training. The PDGA introduce Mahalanobis distance to measure the distance from each sample to its centroid. And based on this, proposes a method that can identify non support vectors and outliers at the same time to help remove redundant data. When the training set is further reduced, cosine angle distance analysis method is proposed to determine whether the samples are redundant data, ensure that the valuable data are not removed. Different from the previous data geometry analysis methods, the PDGA algorithm is implemented in parallel, which greatly saving the computational cost. Experimental results on artificial dataset and 6 real datasets show that the algorithm can adapt to different sample distributions. Which significantly reduce the training time and memory requirements without sacrificing the classification accuracy, and its performance is obviously better than the other five competitive algorithms.</p></abstract>

scholarly journals Body Joints and Trajectory Guided 3D Deep Convolutional Descriptors for Human Activity Identification

2019 ◽  
Vol 8 (12) ◽  
pp. 1016-1021
Keyword(s):  
Optical Flow ◽  
Human Activity ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Computational Cost ◽  
The Body ◽  
Support Vector ◽  
3D Network ◽  
Body Joints ◽  
Body Joint

Human Activity Identification (HAI) in videos is one of the trendiest research fields in the computer visualization. Among various HAI techniques, Joints-pooled 3D-Deep convolutional Descriptors (JDD) have achieved effective performance by learning the body joint and capturing the spatiotemporal characteristics concurrently. However, the time consumption for estimating the locale of body joints by using large-scale dataset and computational cost of skeleton estimation algorithm were high. The recognition accuracy using traditional approaches need to be improved by considering both body joints and trajectory points together. Therefore, the key goal of this work is to improve the recognition accuracy using an optical flow integrated with a two-stream bilinear model, namely Joints and Trajectory-pooled 3D-Deep convolutional Descriptors (JTDD). In this model, an optical flow/trajectory point between video frames is also extracted at the body joint positions as input to the proposed JTDD. For this reason, two-streams of Convolutional 3D network (C3D) multiplied with the bilinear product is used for extracting the features, generating the joint descriptors for video sequences and capturing the spatiotemporal features. Then, the whole network is trained end-to-end based on the two-stream bilinear C3D model to obtain the video descriptors. Further, these video descriptors are classified by linear Support Vector Machine (SVM) to recognize human activities. Based on both body joints and trajectory points, action recognition is achieved efficiently. Finally, the recognition accuracy of the JTDD model and JDD model are compared.

scholarly journals SVM-Based Multiple Instance Classification via DC Optimization

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 249 ◽  
Author(s):  
Annabella Astorino ◽  
Antonio Fuduli ◽  
Giovanni Giallombardo ◽  
Giovanna Miglionico
Keyword(s):  
Binary Classification ◽  
Error Function ◽  
Multiple Instance Learning ◽  
Classification Error ◽  
Support Vector ◽  
Data Sets ◽  
Training Set ◽  
Dc Algorithm ◽  
Difference Of Convex ◽  
Dc Decomposition

A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported.

EVOLVING EDITED k-NEAREST NEIGHBOR CLASSIFIERS

2008 ◽  
Vol 18 (06) ◽  
pp. 459-467 ◽  
Author(s):  
ROBERTO GIL-PITA ◽  
XIN YAO
Keyword(s):  
Error Rate ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Computational Cost ◽  
Letter Recognition ◽  
Classification Error ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Classification Error Rate ◽  
Nearest Neighbor Classifiers

The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate. In recent works, genetic algorithms have been successfully applied to determine which patterns must be included in the edited subset. In this paper we propose a novel implementation of a genetic algorithm for designing edited k-nearest neighbor classifiers. It includes the definition of a novel mean square error based fitness function, a novel clustered crossover technique, and the proposal of a fast smart mutation scheme. In order to evaluate the performance of the proposed method, results using the breast cancer database, the diabetes database and the letter recognition database from the UCI machine learning benchmark repository have been included. Both error rate and computational cost have been considered in the analysis. Obtained results show the improvement achieved by the proposed editing method.

Pattern Recognition of the Steel Rod Based on Artificial Neural Network and SVM

2011 ◽  
Vol 301-303 ◽  
pp. 329-333
Author(s):  
Hong Chun Sun
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Recognition Rate ◽  
Support Vector ◽  
Artificial Neural ◽  
Defects Identification ◽  
The Time Domain ◽  
Frequency Band Energy

The steel rod is an important part for project fields , and it is large-scale to be used. it is apt to crack, corrosion and so on in the poor working conditions. In order to recognize correctly the type of defects, a method was presented to extract frequency band energy feature by using wavelet package decomposition. In the meantime, to extract the peak-peak value in the time-domain and make the mixed feature vector. With the way of pattern recognition, the best recognition way was got by comparing the BP artificial neural network(ANN), PNN(probability neural network) artificial neural network and "one-versus-one" support vector machine(SVM).The result showed that the recognition rate of SVM was more suitable for defects’ identification in steel rod.

A Novel Method for Gas-Liquid Flow Pattern Recognition Based on PDF and SVM

2019 ◽  
Author(s):  
Dong-Xu Wang ◽  
Qi-Hui Hu ◽  
Yu-Xing Li ◽  
Quan Wang ◽  
Shuang Li
Keyword(s):  
Pattern Recognition ◽  
Flow Pattern ◽  
Slug Flow ◽  
Support Vector ◽  
Liquid Holdup ◽  
Horizontal Pipes ◽  
Pattern Recognition Methods ◽  
Novel Method ◽  
Gas Liquid Flow ◽  
Probability Density Function Pdf

Abstract Currently, most of the traditional flow pattern recognition methods are based on the features of differential pressure signal, and the verification data is majorly derived from the horizontal or almost horizontal pipes. This paper proposes a new method that combines the probability density function (PDF) of the liquid holdup signal and the support vector machine (SVM). Results demonstrate the capability of the proposed algorithm to effectively recognize the stratified flow, bubble flow, slug flow, and severe slug flow and eliminate subjective factors.

GAS-LIQUID FLOW PATTERN RECOGNITION USING DIMENSIONLESS NUMBERS

2019 ◽  
Author(s):  
Caio Araujo ◽  
Tiago Ferreira Souza ◽  
Maurício Figueiredo ◽  
valdir estevam ◽  
Ana Maria Frattini Fileti
Keyword(s):  
Pattern Recognition ◽  
Flow Pattern ◽  
Liquid Flow ◽  
Dimensionless Numbers ◽  
Gas Liquid Flow
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