In this paper, we propose support vector-based supervised learning algorithms, called multiclass support vector data description with weighted dynamic time warping kernel function (MSVDD-WDTWK) and multiclass support vector machines with weighted dynamic time warping kernel function (MSVM-WDTWK), which provides a flexible and robust kernel function for time series classification between non-aligned time series data resulting in improved accuracy. The proposed WDTW kernel function provides an optimal match between two time series data by not only allowing a non-linear mapping between two data sequences, but also considering relative significance depending on the phase difference between points on time series data. We validate the proposed approaches using extensive numerical experiments on a number of multiclass UCR time series data mining archive, and demonstrate that our proposed methods provide lower classification error rates compared with existing techniques.

Support vector-based algorithms with weighted dynamic time warping kernel function for time series classification

This work presents a comparison among four methodologies for fault detection and feature selection applied to dimensional quality control in an automobile motor-head machining process. Three of them are based on the Mahalanobis Taguchi System (MTS) while the fourth one uses Support Vector Data Description (SVDD) one-class classification in order to build an hypersphere with the minimum volume with an enclosed boundary containing almost all target objects. Moreover, Gompertz binary particle swarm optimization (GBPSO) algorithm is applied to optimize kernel hyperparameters in SVDD and simultaneously solve the feature selection problem.

Fault detection and optimal feature selection in automobile motor-head machining process

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research. 

https://doi.org/10.1016/j.csbj.2022.05.050

Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra

To effectively ensure the operational safety of an electric vehicle with in-wheel motor drive, a novel diagnosis method is proposed to monitor each in-wheel motor fault, the creativity of which lies in two aspects. One aspect is that affinity propagation (AP) is introduced into a minimum-distance discriminant projection (MDP) algorithm to propose a new dimension reduction algorithm, which is defined as APMDP. APMDP not only gathers the intra-class and inter-class information of high-dimensional data but also obtains information on the spatial structure. Another aspect is that multi-class support vector data description (SVDD) is improved using the Weibull kernel function, and its classification judgment rule is modified into a minimum distance from the intra-class cluster center. Finally, in-wheel motors with typical bearing faults are customized to collect vibration signals under four operating conditions, respectively, to verify the effectiveness of the proposed method. The results show that the APMDP’s performance is better than traditional dimension reduction methods, and the divisibility is improved by at least 8.35% over the LDA, MDP, and LPP. A multi-class SVDD classifier based on the Weibull kernel function has high classification accuracy and strong robustness, and the classification accuracies of the in-wheel motor faults in each condition are over 95%, which is higher than the polynomial and Gaussian kernel function.

/pdf/in-wheel-motor-fault-diagnosis-using-affinity-propagation-202x8516.pdf

In-Wheel Motor Fault Diagnosis Using Affinity Propagation Minimum-Distance Discriminant Projection and Weibull-Kernel-Function-Based SVDD

With the development of computer technology, data mining, artificial intelligence, and image-processing technology have been applied to medical diagnosis. Image classification is one of the main technologies of medical image processing, which can be used to determine whether a patient suffers from breast cancer according to x-ray images of the breast. To achieve reliable classification of breast images, an image classification method combined with a fusion Gaussian–Hermite moments feature and improved nonlinear support vector machine (SVM) classifier is proposed. The proposed fusion Gaussian–Hermite moments features can improve the robustness and distinguish the ability of features by constructing Gaussian–Hermite invariant moments according to invariant moment theory and constructing a Gaussian–Hermite Fisher moment according to Fisher’s idea. The proposed improved nonlinear SVM classifier can improve the efficiency and accuracy of the classifier through eigen decomposition and sample learning. Experimental results demonstrate that the proposed method has a high accuracy rate for breast x-ray image classification.

Image Classification Combined with Fusion Gaussian–Hermite Moments Feature and Improved Nonlinear SVM Classifier

A majority of effective approaches are presented in classification of mammography. In this paper we propose the Hyper sphere Multi-Class Support Vector Data Description (HSMC-SVDD) approach, in order to improve the classification accuracy and training speed when the categories have been increased to more than two classes. The main idea of the HSMC-SVDD is to extend a Hyper sphere One-Class SVDD (HSOC-SVDD) to a HSMC-SVDD as a novel kind of immediate multiple classifiers. Experimental results on the Mammographic Image Analysis Society (MIAS) dataset show that the average training time is 21.369 seconds, compared with the combined classifier proposed by Wei, the training speed has been improved from 10 to 20 seconds and the average testing time is 0.4281 seconds by our approach. And our method can provide 76.6929% classification accuracy.

A Multi-class Support Vector Data Description Approach for Classification of Medical Image

New medical image classification approach based on hypersphere multi-class support vector data description: New medical image classification approach based on hypersphere multi-class support vector data description

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A Multi-class Support Vector Data Description Approach for Classification of Medical Image

New medical image classification approach based on hypersphere multi-class support vector data description: New medical image classification approach based on hypersphere multi-class support vector data description