IMPROVING SVM PERFORMANCE IN MULTI-LABEL DOMAINS: THRESHOLD ADJUSTMENT

2013 ◽  
Vol 22 (01) ◽  
pp. 1250038 ◽  
Author(s):  
PEERAPON VATEEKUL ◽  
SAREEWAN DENDAMRONGVIT ◽  
MIROSLAV KUBAT
Keyword(s):  
Classification Performance ◽  
Support Vector ◽  
Binary Classifier ◽  
Research Groups ◽  
Computational Costs ◽  
Vector Machines ◽  
Threshold Adjustment ◽  
Good Classification ◽  
Good Classification Performance ◽  
Suboptimal Behavior

In “multi-label domains,” where the same example can simultaneously belong to two or more classes, it is customary to induce a separate binary classifier for each class, and then use them all in parallel. As a result, some of these classifiers are induced from imbalanced training sets where one class outnumbers the other – a circumstance known to hurt some machine learning paradigms. In the case of Support Vector Machines (SVM), this suboptimal behavior is explained by the fact that SVM seeks to minimize error rate, a criterion that is in domains of this type misleading. This is why several research groups have studied mechanisms to readjust the bias of SVM's hyperplane. The best of these achieves very good classification performance at the price of impractically high computational costs. We propose here an improvement where these cost are reduced to a small fraction without significantly impairing classification.

scholarly journals Coupled Least Squares Support Vector Ensemble Machines

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 195 ◽  
Author(s):  
Dickson Keddy Wornyo ◽  
Xiang-Jun Shen
Keyword(s):  
Least Squares ◽  
Classification Performance ◽  
Support Vector ◽  
Vector Method ◽  
Wide Range ◽  
Good Classification ◽  
Coupling Strategy ◽  
Good Classification Performance ◽  
Data Driven Modeling ◽  
Model Approach

The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The proposed coupling ensemble helps improve robustness and produce good classification performance than the single model approach. The proposed C-LSSVEM can choose appropriate kernel types and their parameters in a good coupling strategy with a set of classifiers being trained simultaneously. The proposed method can further minimize the total loss of ensembles in kernel space. Thus, we form an ensemble regressor by co-optimizing and weighing base regressors. Experiments conducted on several datasets such as artificial datasets, UCI classification datasets, UCI regression datasets, handwritten digits datasets and NWPU-RESISC45 datasets, indicate that C-LSSVEM performs better in achieving the minimal regression loss and the best classification accuracy relative to selected state-of-the-art regression and classification techniques.

Application of WAVELET-SVM in Fault Diagnosis for the UV Control System

2011 ◽  
Vol 65 ◽  
pp. 199-203
Author(s):  
Sheng Wu Wang ◽  
Xiu Hua Shi ◽  
Hui Xu ◽  
Zhao Jing Tong
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Wavelet Transformation ◽  
Classification Performance ◽  
Support Vector ◽  
Fault Features ◽  
Good Classification ◽  
Good Classification Performance ◽  
Simulation Results ◽  
Fault Characteristic

Wavelet Analysis extracts the main feature from the fault signal through wavelet transformation, so it is advantageous to withdraw fault characteristic for fault diagnosis. Support Vector Machine (SVM) has shown its good classification performance in fault diagnosis. A new method of fault diagnosis for UV control system based on WAVELET-SVM is raised. The sensor output is sampled in frequency domain and it is preprocessed by wavelet to extract main vectors of the fault features. Fault patterns under various states are classified using multi-class SVM, and fault diagnosis is realized. The simulation results show that WAVELET-SVM is feasible to detect and locate faults quickly and exactly and has high robustness.

scholarly journals Creating a Chinese suicide dictionary for identifying suicide risk on social media

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1455 ◽  
Author(s):  
Meizhen Lv ◽  
Ang Li ◽  
Tianli Liu ◽  
Tingshao Zhu
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Classification Performance ◽  
Support Vector ◽  
Accurate Identification ◽  
Vector Machines ◽  
Social Media Service ◽  
Linguistic Inquiry ◽  
Suicide Prevention Programs ◽  
Expert Ratings

Introduction.Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary.Methods.To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models.Results and Discussion.Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r= 0.507) and evaluating individual suicide risk (r= 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1:F1= 0.48; t2:F1= 0.56) produced a more accurate identification than SCLIWC (t1:F1= 0.41; t2:F1= 0.48) on different observation windows.Conclusions.This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries.

scholarly journals Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit

2021 ◽  
Vol 39 (4) ◽  
pp. 1190-1197
Author(s):  
Y. Ibrahim ◽  
E. Okafor ◽  
B. Yahaya
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Face Recognition ◽  
Regularization Parameter ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Support Vector ◽  
Vector Machines ◽  
Linear Svm ◽  
Learning Architectures

Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance. Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector  Machines.

scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

scholarly journals On the parameter optimization of Support Vector Machines for binary classification

2012 ◽  
Vol 9 (3) ◽  
pp. 33-43 ◽  
Author(s):  
Paulo Gaspar ◽  
Jaime Carbonell ◽  
José Luís Oliveira
Keyword(s):  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Performance ◽  
Biological Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Minimal Risk ◽  
Class Separation ◽  
Vector Machines ◽  
Analyse Data

Summary Classifying biological data is a common task in the biomedical context. Predicting the class of new, unknown information allows researchers to gain insight and make decisions based on the available data. Also, using classification methods often implies choosing the best parameters to obtain optimal class separation, and the number of parameters might be large in biological datasets.Support Vector Machines provide a well-established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set and the tuning of hyper-parameters. Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive.In this paper we review the strategies that are used to improve the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

2005 ◽  
Vol 127 (3) ◽  
pp. 294-303 ◽  
Author(s):  
Piervincenzo Rizzo ◽  
Ivan Bartoli ◽  
Alessandro Marzani ◽  
Francesco Lanza di Scalea
Keyword(s):  
Guided Waves ◽  
Fourier Transforms ◽  
Damage Index ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Cross Sectional ◽  
Classification Framework ◽  
Discrete Wavelet Decomposition ◽  
Good Classification ◽  
Good Classification Performance

This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.

scholarly journals Selection of Support Vector Candidates Using Relative Support Distance for Sustainability in Large-Scale Support Vector Machines

2020 ◽  
Vol 10 (19) ◽  
pp. 6979
Author(s):  
Minho Ryu ◽  
Kichun Lee
Keyword(s):  
Support Vector Machines ◽  
Quadratic Programming ◽  
Decision Trees ◽  
Programming Problem ◽  
Large Scale ◽  
Classification Performance ◽  
Quadratic Programming Problem ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines

Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin. In the computation of the hyperplane, however, it is necessary to solve a quadratic programming problem. The storage cost of a quadratic programming problem grows with the square of the number of training sample points, and the time complexity is proportional to the cube of the number in general. Thus, it is worth studying how to reduce the training time of SVMs without compromising the performance to prepare for sustainability in large-scale SVM problems. In this paper, we proposed a novel data reduction method for reducing the training time by combining decision trees and relative support distance. We applied a new concept, relative support distance, to select good support vector candidates in each partition generated by the decision trees. The selected support vector candidates improved the training speed for large-scale SVM problems. In experiments, we demonstrated that our approach significantly reduced the training time while maintaining good classification performance in comparison with existing approaches.

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