Optimizing Training Data and Hyperparameters of Support Vector Machines Using a Memetic Algorithm

2019 ◽  
pp. 229-238
Author(s):  
Wojciech Dudzik ◽  
Michal Kawulok ◽  
Jakub Nalepa
Keyword(s):  
Support Vector Machines ◽  
Memetic Algorithm ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines

scholarly journals DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS

2016 ◽  
Vol III-7 ◽  
pp. 89-96 ◽  
Author(s):  
Ribana Roscher ◽  
Jan Behmann ◽  
Anne-Katrin Mahlein ◽  
Jan Dupuis ◽  
Heiner Kuhlmann ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Sparse Representation ◽  
Test Data ◽  
Training Data ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Support Vector ◽  
Leaf Spot Disease ◽  
Disease Symptoms ◽  
Vector Machines

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of <i>Cercospora</i> leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

2020 ◽  
Vol 4 (5) ◽  
pp. 915-922
Author(s):  
Helena Nurramdhani Irmanda ◽  
Ria Astriratma
Keyword(s):  
Support Vector Machines ◽  
Training Data ◽  
Classification Model ◽  
Support Vector ◽  
Processing Stage ◽  
Stop Word ◽  
Vector Machines ◽  
Testing Data ◽  
Extraction Stage ◽  
Multiclass Svm

This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.  

Fuzzy Asymmetric Support Vector Machines

2012 ◽  
Vol 433-440 ◽  
pp. 7479-7486
Author(s):  
Rui Kong ◽  
Qiong Wang ◽  
Gu Yu Hu ◽  
Zhi Song Pan
Keyword(s):  
Support Vector Machines ◽  
Medical Diagnosis ◽  
Credit Card ◽  
Training Data ◽  
Support Vector ◽  
Imbalanced Datasets ◽  
Credit Card Fraud ◽  
Vector Machines ◽  
Imbalanced Training Data ◽  
Error Costs

Support Vector Machines (SVM) has been extensively studied and has shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in medical diagnosis and detecting credit card fraud). In this paper, we propose the fuzzy asymmetric algorithm to augment SVMs to deal with imbalanced training-data problems, called FASVM, which is based on fuzzy memberships, combined with different error costs (DEC) algorithm. We compare the performance of our algorithm against these two algorithms, along with different error costs and regular SVM and show that our algorithm outperforms all of them.

Support vector machines for detection and characterization of rolling element bearing faults

2001 ◽  
Vol 215 (9) ◽  
pp. 1065-1074 ◽  
Author(s):  
L. B. Jack ◽  
A. K. Nandi
Keyword(s):  
Support Vector Machines ◽  
Vibration Signal ◽  
Training Data ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Statistical Estimates ◽  
Vector Machines ◽  
Rolling Element ◽  
Fault Characterization

Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical estimates of the vibration signal as input features, and they have been shown to be highly successful in this type of application. Support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring (CM) arena. The availability of a limited amount of training data creates some problems for the use of SVMs, and a strategy is offered that improves the generalization performance significantly in cases where only limited training data are available. This paper examines the performance of both types of classifier in one given scenario—a multiclass fault characterization example.

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