Local Support Vector Research Articles

A general procedure for evaluating models and ensemble Support Vector Regression

In practice, we may want to discover if there is a relationship between a response variable as a function of the predictor variables. Multiple linear regression (MLR) is a popular tool for such purpose. When the relationship is nonlinear, nonparametric regression methods such as local linear regression, smoothing splines, and support vector regression (SVR) provide flexible alternatives to MLR. How do we compare the performance of these methods and choose an appropriate one for use? In this research, we propose a general procedure to evaluate the performance of different regression methods for use in large data. We also propose an ensemble SVR for regression analysis. The proposed methods are applied to address research questions using the Research and Development Survey, conducted by the National Center for Health Statistics.

Calibrating lab and field reflectance spectra for nutrient estimation in potato plants using local support vector regression models

This study presents a methodology based on multiple local support vector regression (SVR) to calibrate the spectra taken in the field in relative to lab-derived spectra. Laboratory based foliar spectral measurement is a common method to provide lab-derived spectra as a service where a grower sends sample leaves collected manually. The drawback of this method is being time-consuming when the samples are collected and analyzed. In contrast, in-field spectral measurements can be an alternative method capable of providing immediate readings. While both methods work based on the same priniciple, the insturmental differences as well as the conditional difference under which the instruments operate may cause differences in the spectral patterns of the same target. In this work, after developing the calibration method, we validated it by estimating NPK measurements in potato plants using in-field, lab, and field calibrated spectral measurements over two testing modes: dried and fresh. The results showed that the calibration using SVR models could minimize the percentage relative error (PRE) between lab and field spectra within the visible range by considering the influence of the neighboring wavebands up to 32 nm width which improved the alignment of the local maxima of the specral curves. Also, a substantial PRE reduction from 120 % to 20 % for some wavebands in the short-wave infrared (SWIR) region of the fresh mode was observed due to the influence of scaling within the SVR method. The calibration improved the alignment of NPK estimated values between lab and field calibrated spectra of both modes with an emphasis on its necessity to estimate nutrients in the fresh mode as the root mean square error was < 0.1 for the three elements.

Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of literature poses an obstacle to a complete understanding in this area of interest. The methodologies studied were systematic mapping and systematic literature review. For each article, the journal, conference, year of publication, dataset, breast cancer characteristics, PET/CT processing techniques, metrics and diagnostic yield results were identified. Sixty-four articles were analyzed, 44 (68.75%) belong to journals and 20 (31.25%) belong to the conference category. A total of 102 techniques were identified, which were distributed in preprocessing with 7 (6.86%), segmentation with 15 (14.71%), feature extraction with 15 (14.71%), and classification with 65 (63.73%). The techniques with the highest incidence identified in each stage are: Gaussian Filter, SLIC, Local Binary Pattern, and Support Vector Machine with 4, 2, 7, and 35 occurrences, respectively. Support Vector Machine is the predominant technique in the classification stage, due to the fact that Artificial Intelligence is emerging in medical image processing and health care to make expert systems increasingly intelligent and obtain favorable results.

Recognition of Angry and Happy Facial Expressions with Local Binary Pattern and Support Vector Machine

Looks are a huge sort of nonverbal correspondence for getting a handle on a singular's opinions and sentiments. Look affirmation has various potential applications, including security, tutoring, and prosperity systems. The justification for this assessment is for a look affirmation system using area twofold model and sponsorship vector machine techniques. It is typical that this structure can perceive two kinds of sentiments, to be explicit angry and delighted to get the right assumption. First accumulate fundamental data and assistant data from Kaggle and phone cameras, second preprocessing data with grayscale and resizing, third surface features using Area equal model extraction, fourth assist vector with machining about look request used, finally structure evaluation considering precision, exactness, survey, and F-1 worth. The results show that the look affirmation system works honorably on area matched model and sponsorship vector machine. On this investigation dataset, support vector machine gathering on direct part has favored execution over winding reason ability segment. The most significant precision was achieved with the straight part at 86.25%. This study assumes that the look affirmation structure can work outstandingly with neighborhood matched model and sponsorship vector machine. The straight part shows better execution on this investigation dataset. This assessment can be also advanced by adding more looks, incorporate extraction methods, and plan.

Diagnosing Faults in Rolling Bearings of an Air Compressor Set Up Using Local Mean Decomposition and Support Vector Machine Algorithm

Diagnosing Faults in Rolling Bearings of an Air Compressor Set Up Using Local Mean Decomposition and Support Vector Machine Algorithm

Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition

ABSTRACT In information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine.

Artificial intelligence has revolutionised smart medicine, resulting in enhanced medical care. This study presents an automated detector chip for age-related macular degeneration (AMD) using a support vector machine (SVM) and three-dimensional (3D) optical coherence tomography (OCT) volume. The aim is to assist ophthalmologists by reducing the time-consuming AMD medical examination. Using the property of 3D OCT volume, a modified feature vector connected method called slice-sum is proposed, reducing computational complexity while maintaining high detection accuracy. Compared to previous methods, this method significantly reduces computational complexity by at least a hundredfold. Image adjustment and noise removal steps are excluded for classification accuracy, and the feature extraction algorithm of local binary patterns is determined based on hardware consumption considerations. Through optimisation of the feature vector connection method after feature extraction, the computational complexity of SVM detection is significantly reduced, making it applicable to similar 3D datasets. Additionally, the design supports model replacement, allowing users to train and update classification models as needed. Using TSMC 40 nm CMOS technology, the proposed detector achieves a core area of 0.12 mm2 while demonstrating a classification throughput of 8.87 decisions/s at a maximum operating frequency of 454.54 MHz. The detector achieves a final testing classification accuracy of 92.31%.

A parallel and balanced SVM algorithm on spark for data-intensive computing

Support Vector Machine (SVM) is a machine learning with excellent classification performance, which has been widely used in various fields such as data mining, text classification, face recognition and etc. However, when data volume scales to a certain level, the computational time becomes too long and the efficiency becomes low. To address this issue, we propose a parallel balanced SVM algorithm based on Spark, named PB-SVM, which is optimized on the basis of the traditional Cascade SVM algorithm. PB-SVM contains three parts, i.e., Clustering Equal Division, Balancing Shuffle and Iteration Termination, which solves the problems of data skew of Cascade SVM and the large difference between local support vector and global support vector. We implement PB-SVM in AliCloud Spark distributed cluster with five kinds of public datasets. Our experimental results show that in the two-classification test on the dataset covtype, compared with MLlib-SVM and Cascade SVM on Spark, PB-SVM improves efficiency by 38.9% and 75.4%, and the accuracy is improved by 7.16% and 8.38%. Moreover, in the multi-classification test, compared with Cascade SVM on Spark on the dataset covtype, PB-SVM improves efficiency and accuracy by 94.8% and 18.26% respectively.

Image splicing detection with principal component analysis generated low-dimensional homogeneous feature set based on local binary pattern and support vector machine

Image splicing detection with principal component analysis generated low-dimensional homogeneous feature set based on local binary pattern and support vector machine

Active Incremental Learning for Health State Assessment of Dynamic Systems With Unknown Scenarios

This article is concerned with the data-driven health state assessment task with unknown scenarios. Unknown scenarios are inaccessible in the stage of model training but they can appear unexpectedly during the running stage. A new problem called the within-class distribution mismatch is raised by assuming that unknown scenarios still belong to known classes. To tackle this challenging problem, a novel active incremental learning scheme with a classifier and an out-of-distribution (OOD) detector is proposed. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -fast incremental support vector data description ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -FISVDD) model is put forward as the OOD detector to recognize distribution mismatch samples online for label annotation. Specifically, it integrates the clustering algorithm to build local support vector sets, based on which an active query strategy is developed. An incremental learning mechanism is also designed to reduce the labeling cost. Then, the new labeled data can simultaneously refine the classifier and OOD detector. Two cases, including a bearing benchmark dataset and the operation data of a practical deep-sea manned submersible, are studied to demonstrate the effectiveness of the proposed scheme.

Gas Pipeline Leakage Detection Method Based on IUPLCD and GS-TBSVM

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental results show that IUPLCD can effectively filter the noise in the signal and GS-TBSVM can accurately judge the working conditions of the gas pipeline, with a maximum identification accuracy of 98.4%.

A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering

A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering

Enhancing Human Expression Classification using Local Binary Patterns and Support Vector Machines with RBF Kernel in Convolutional Neural Networks

Enhancing Human Expression Classification using Local Binary Patterns and Support Vector Machines with RBF Kernel in Convolutional Neural Networks

Motor bearing fault diagnosis based on local characteristic-scale decomposition and support vector optimized by sine cosine algorithm

When the motor bearing fails, the nonlinear and unstable characteristics of the vibration signal will lead to a low fault identification accuracy. This paper proposes a fault diagnosis method for motor bearings based on local characteristic-scale decomposition (LCD) and sine-cosine algorithm to optimize support vector machine. In this paper, the LCD algorithm is used to decompose the vibration signal, and screened out the effective intrinsic scale components (ISC) component. Then, Sample Entropy and Permutation Entropy are extracted from the filtered components. For the selection of parameters c and g of SVM, this paper introduces the sine and cosine optimization algorithm (SCA) for position update optimization, and builds an SVM model to obtain a classification model for motor bearing faults after training on the data set. After importing the remaining test data set into the classification model for testing, the fault classification accuracy results of the motor bearing can be obtained. The results of the simulation experiments show that the recognition rate of motor bearing faults based on LCD and SCA-SVM is 98.75%.

Local support vector machine based dimension reduction

AbstractMotivated by several recent work that adopt support vector machines into the sufficient dimension reduction research, we propose a local support vector machine based dimension reduction approach. The proposal deals with continuous and binary responses, linear and nonlinear dimension reduction in a unified framework. The localization can also help relax the stringent probabilistic assumptions required by the global methods. Numerical experiments and a real data application demonstrate the efficacy of the proposed approach.

Measurement Error Prediction of Power Metering Equipment Using Improved Local Outlier Factor and Kernel Support Vector Regression

The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

Analysis and Detection of Fraud in Credit Card Using SILOF

Nowadays e-commerce and online transaction is growing rapidly. For online and offline transaction most of the customer uses credit card. Credit card used globally for online transaction, buy goods, product, and payment. The rising use of credit card can increase the chances of fraud in credit card. Credit card system is at risk now. The effect of this fraudulent transaction is on the bank and institute causing a financial loss to them. For the detection of distinguish frauds, several machine learning models are utilized for better prediction. The major objective of this article is to identify the fraudulent transaction and outlier in credit card transaction. The dataset of credit card is unbalanced. There are various techniques by which fraudulent transaction can be detected and we have used these techniques such as isolation forest method, local outlier factor and support vector machine to determine fraud in credit card. We have used different matrices for enhancing the performance and accuracy. At last comparison analysis is done by using isolation forest, support vector machine, and local outlier which give the better result.

Improving TVB-N prediction in pork using portable spectroscopy with just-in-time learning model updating method

Near infrared spectroscopy (NIR) technology is an effective method for nondestructive prediction of total volatile basic nitrogen (TVB-N) in pork. However, the NIR models lack robustness and often fail when used on a new batch. To handle the problem and obtain better prediction performance, a model updating method based on just-in-time learning (JITL) was proposed in this study. A comprehensive similarity criterion considering both input (spectra) and output (TVB-N content) information was designed. Combining a defined similarity factor, the most relevant samples to new batch samples were selected and a local least square support vector machine model was established in real time based on the selected samples. The results showed that the models updated with JITL approach kept a high predictive performance on new independent batch with prediction error decreasing from 2.95 to 1.60 mg/100 g. The robust models made on selected similar samples combined with JITL model updating strategy can support to make NIR spectroscopy a preferred choice for non-destructive assessment of quality features in pork meat.

Image splicing detection using discriminative robust local binary pattern and support vector machine

PurposeThis paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image.Design/methodology/approachThe structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged.FindingsThe performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique.Originality/valueThe proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.

Classification of breast cancer images using completed local ternary pattern and support vector machine

Breast cancer is the major cause of deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local binary pattern (LBP) and local ternary pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and completed local binary count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.