scholarly journals An Improved Immune Clone Selection Algorithm for Palm Bio-impedance Spectroscopy

2015 ◽  
Vol 16 (2) ◽  
pp. 363
Author(s):  
LinTao LV ◽  
QinQin Yuan ◽  
YuXiang Yang
Keyword(s):  
Impedance Spectroscopy ◽  
Least Squares Method ◽  
Feature Model ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Svm Classification ◽  
Optimal Feature Subset ◽  
Optimal Feature ◽  
Immune Clone Selection

A kind of effective feature model of palm BIS data is presented according to the features of Palm bio-impedance spectroscopy (BIS) data. Based on immune clone algorithm and least squares method, an improved palm BIS feature selection algorithm is established,which can be applied to obtain the optimal feature subset that can be completely represented the palm BIS data, Finally, the algorithm is compared with other algorithms. The experimental results show that the accuracy of the feature subset obtained by the algorithm has reached 93.2 in SVM classification algorithm test.Therefore, the algorithm in this article is valid and reliable , which is of high theoretical and practical value.

Feature Selection Algorithm for Palm Bio-Impedance Spectroscopy Based on Immune Clone

2013 ◽  
Vol 347-350 ◽  
pp. 2712-2716
Author(s):  
Lin Tao Lü ◽  
Peng Li ◽  
Yu Xiang Yang ◽  
Fang Tan
Keyword(s):  
Feature Selection ◽  
Impedance Spectroscopy ◽  
Least Squares Method ◽  
Feature Model ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Elliptical Model ◽  
Optimal Feature Subset ◽  
Optimal Feature

According to the features of Palm bio-impedance spectroscopy (BIS) data, this paper suggests a kind of effective feature model of palm BIS data elliptical model. The model combines immune clone algorithm and least squares method, establishes a palm BIS feature selection algorithm, and uses the algorithm to obtain the optimal feature subset that can completely represent the palm BIS data, and then use several classification algorithms for classification and comparison. The experimental results show that accuracy of the feature subset obtained through the algorithm in SVM classification algorithm test can reach 93.2, thereby verifying the algorithm is a valid and reliable palm BIS feature selection algorithm.

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

Feature Subset Selection Based on the Genetic Algorithm

2013 ◽  
Vol 774-776 ◽  
pp. 1532-1537
Author(s):  
Jing Wei Yang ◽  
Si Le Wang ◽  
Ying Yi Chen ◽  
Su Kui Lu ◽  
Wen Zhu Yang
Keyword(s):  
Feature Subset Selection ◽  
Initial Population ◽  
Feature Subset ◽  
Crossover Operator ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Fitness Value ◽  
Optimal Feature Subset ◽  
Crossover And Mutation ◽  
Optimal Feature

This paper presents a genetic-based feature selection algorithm for object recognition. Firstly, the proposed algorithm encodes a solution with a binary chromosome. Secondly, the initial population was generated randomly. Thirdly, a crossover operator and a mutation operator are employed to operate on these chromosomes to generate more competency chromosomes. The probability of the crossover and mutation are adjusted dynamically according to the generation number and the fitness value. The proposed algorithm is tested using the features extracted from cotton foreign fiber objects. The results indicate that the proposed algorithm can obtain the optimal feature subset, and can reduce the classification time while keeping the classification accuracy constant.

Reseach on Feature Selection Algorithm Based on the Margin of Support Vector Machine

2013 ◽  
Vol 333-335 ◽  
pp. 1430-1434
Author(s):  
Lin Fang Hu ◽  
Lei Qiao ◽  
Min De Huang
Keyword(s):  
Feature Selection ◽  
Weather Forecast ◽  
Support Vector ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Optimal Hyperplane ◽  
Optimal Feature Subset ◽  
Optimal Feature

A feature selection algorithm based on the optimal hyperplane of SVM is raised. Using the algorithm, the contribution to the classification of each feature in the candidate feature set is test, and then the feature subset with best classification ability will be selected. The algorithm is used in the recognition process of storm monomers in weather forecast, and experimental data show that the classification ability of the features can be effectively evaluated; the optimal feature subset is selected to enhance the working performance of the classifier.

Fault detection in mixture production process based on wavelet packet and support vector machine

2021 ◽  
pp. 1-15
Author(s):  
Yan Chen ◽  
Huan-sheng song ◽  
Yan-ni yang ◽  
Gang-feng wang
Keyword(s):  
Production Process ◽  
Wavelet Packet ◽  
Road Construction ◽  
Support Vector ◽  
Feature Subset ◽  
Svm Classification ◽  
De Algorithm ◽  
Optimal Feature Subset ◽  
Optimal Feature

Mixture production equipment is widely employed in road construction, and the quality of the produced mixture is the essential factor to ensure the quality of road construction. To detect the quality of the real-time produced mixture and solve the shortcomings of laboratory detection lag, a new fault detection method in the mixture production process is proposed, which is based on wavelet packet decomposition (WPD) and support vector machine (SVM). The proposed scheme includes feature extraction, feature selection, SVM classification, and optimization algorithm. During feature extraction, wavelet basis function is utilized to 4-layer decompose the aggregate and asphalt data mixed in real-time. The energy value calculated by wavelet packet coefficient is the extracted feature. During feature selection, a method combining the chi-square test and wrapper (CSW) is conducted to select the optimal feature subset from WPD features. Eventually, by adopting the optimal feature subset, SVM has been developed to classify various faults. Its parameters are optimized by differential evolution (DE) algorithm. In the test stage, multiple faults of different specifications of aggregates and asphalt are detected in the mixture production process. The results demonstrate that (1) accuracy produced by the CSW method with WPD features is 4.33% higher than the PCA method with statistical features; (2) SVM classification method optimized by DE algorithm brings an increase in recognition accuracy of identifying different types of mixture production faults produced by different equipment. Compared to other available methods, the proposed algorithm has a very outstanding detection performance.

scholarly journals Efficient Feature Subset Selection Algorithm for High Dimensional Data

2016 ◽  
Vol 6 (4) ◽  
pp. 1880 ◽  
Author(s):  
Smita Chormunge ◽  
Sudarson Jena
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
High Dimensional Data ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Computational Performance ◽  
Optimal Feature Subset

<p>Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Existing Feature selection algorithms take more time to obtain feature subset for high dimensional data. This paper proposes a feature selection algorithm based on Information gain measures for high dimensional data termed as IFSA (Information gain based Feature Selection Algorithm) to produce optimal feature subset in efficient time and improve the computational performance of learning algorithms. IFSA algorithm works in two folds: First apply filter on dataset. Second produce the small feature subset by using information gain measure. Extensive experiments are carried out to compare proposed algorithm and other methods with respect to two different classifiers (Naive bayes and IBK) on microarray and text data sets. The results demonstrate that IFSA not only produces the most select feature subset in efficient time but also improves the classifier performance.</p>

Efficient Feature Subset Selection Algorithm for High Dimensional Data

2016 ◽  
Vol 6 (4) ◽  
pp. 1880 ◽  
Author(s):  
Smita Chormunge ◽  
Sudarson Jena
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
High Dimensional Data ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Computational Performance ◽  
Optimal Feature Subset

<p>Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Existing Feature selection algorithms take more time to obtain feature subset for high dimensional data. This paper proposes a feature selection algorithm based on Information gain measures for high dimensional data termed as IFSA (Information gain based Feature Selection Algorithm) to produce optimal feature subset in efficient time and improve the computational performance of learning algorithms. IFSA algorithm works in two folds: First apply filter on dataset. Second produce the small feature subset by using information gain measure. Extensive experiments are carried out to compare proposed algorithm and other methods with respect to two different classifiers (Naive bayes and IBK) on microarray and text data sets. The results demonstrate that IFSA not only produces the most select feature subset in efficient time but also improves the classifier performance.</p>

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

scholarly journals Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 478 ◽  
Author(s):  
Jiajin Qi ◽  
Xu Gao ◽  
Nantian Huang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Time Domain ◽  
High Efficiency ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Feature Subset ◽  
High Voltage Circuit Breaker ◽  
Optimal Feature Subset ◽  
Optimal Feature

The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

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