Application of differential evolution for optimization of least-square support vector machine classifier of signal-averaged electrocardiograms

2009 ◽  
Author(s):  
Sebastian Krys ◽  
Stanislaw Jankowski ◽  
Ewa Piatkowska-Janko
Keyword(s):  
Support Vector Machine ◽  
Differential Evolution ◽  
Support Vector Machine Classifier ◽  
Least Square ◽  
Support Vector

Tool condition monitoring system based on support vector machine and differential evolution optimization

2016 ◽  
Vol 231 (5) ◽  
pp. 805-813 ◽  
Author(s):  
Guo F Wang ◽  
Qing L Xie ◽  
Yan C Zhang
Keyword(s):  
Support Vector Machine ◽  
Tool Wear ◽  
Differential Evolution ◽  
Condition Monitoring ◽  
Support Vector Machine Classifier ◽  
Tool Condition Monitoring ◽  
Support Vector ◽  
Grid Search ◽  
Tool Condition ◽  
Condition Monitoring System

A tool condition monitoring system based on support vector machine and differential evolution is proposed in this article. In this system, support vector machine is used to realize the mapping between the extracted features and the tool wear states. At the same time, two important parameters of the support vector machine which are called penalty parameter C and kernel parameter [Formula: see text] are optimized simultaneously based on differential evolution algorithm. In order to verify the effectiveness of the proposed system, a multi-tooth milling experiment of titanium alloy was carried out. Cutting force signals related to different tool wear states were collected, and several time domain and frequency domain features were extracted to depict the dynamic characteristics of the milling process. Based on the extracted features, the differential evolution-support vector machine classifier is constructed to realize the tool wear classification. Moreover, to make a comparison, empirical selection method and four kinds of grid search algorithms are also used to select the support vector machine parameters. At the same time, cross validation is utilized to improve the robustness of the classifier evaluation. The results of analysis and comparisons show that the classification accuracy of differential evolution-support vector machine is higher than empirical selection-support vector machine. Moreover, the time consumption of differential evolution-support vector machine classifier is 5 to 12 times less than that of grid search-support vector machine.

Monitoring of drill runout using Least Square Support Vector Machine classifier

Measurement ◽  
2019 ◽  
Vol 146 ◽  
pp. 24-34 ◽  
Author(s):  
J. Susai Mary ◽  
M.A. Sai Balaji ◽  
A. Krishnakumari ◽  
R.S. Nakandhrakumar ◽  
D. Dinakaran
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Least Square ◽  
Support Vector

scholarly journals An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
E. Chatzimichail ◽  
E. Paraskakis ◽  
M. Sitzimi ◽  
A. Rigas
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Outcome Prediction ◽  
Intelligent System ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Asthma Outcome

Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority.Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation.Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results.Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.

Histogram Based Bacteria Colony Features Analysis

2014 ◽  
Vol 609-610 ◽  
pp. 1448-1452
Author(s):  
Kun Zhang ◽  
Min Rui Fei
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Least Square ◽  
Support Vector ◽  
Least Square Estimation ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. This paper presents a novel approach for adaptive colony segmentation by classifying the detected peaks of intensity histograms of images. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained support vector machine (USVM) has better recognition accuracy than the other state of the art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

Fault Diagnosis of Power Transformer Based on Adaptive Differential Evolution and Least Square Support Vector Machine

2013 ◽  
Vol 791-793 ◽  
pp. 912-916 ◽  
Author(s):  
Zi Pin Li ◽  
Hui Peng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Differential Evolution ◽  
Power Transformer ◽  
Differential Evolution Algorithm ◽  
Classification Problem ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Adaptive Differential Evolution

Least square support vector machine (LS-SVM) can solve small sample, high-dimensional and non-linear multi-classification problem well, so it is applicable to the power transformer fault diagnosis. However, the parameters of LS-SVM have significant effect on the classification results.In this paper, the adaptive differential evolution algorithm (ADE) is applied to optimize the parameters of LS-SVM. The scaling factor and crossover rate are adjusted dynamically in the whole evolution process, so the robustness of the algorithm is improved greatly. The optimized LS-SVM is applied to fault diagnosis of power transformer, the results obtained demonstrate superiority of the proposed approach.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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