Clustering Mass Spectral Peaks Increases Recognition Accuracy and Stability of SVM-based Feature Selection

2010 ◽  
Vol 03 (02) ◽  
pp. 048-054 ◽  
Author(s):  
Mikhail Pyatnitskiy ◽  
Maria Karpova ◽  
Sergei Moshkovskii ◽  
Andrey Lisitsa ◽  
Alexander Archakov
Keyword(s):  
Feature Selection ◽  
Recognition Accuracy ◽  
Mass Spectral ◽  
Spectral Peaks ◽  
Accuracy And Stability

scholarly journals Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Yang Hui ◽  
Xuesong Mei ◽  
Gedong Jiang ◽  
Tao Tao ◽  
Changyu Pei ◽  
...  
Keyword(s):  
Tool Wear ◽  
Frequency Domain ◽  
Recognition Accuracy ◽  
Milling Process ◽  
Ensemble Model ◽  
Vibration Signals ◽  
State Recognition ◽  
Model Based ◽  
Milling Tool ◽  
Accuracy And Stability

Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G-mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.

Comparison of Recognition Accuracy on Dynamic Hand Gesture Using Feature Selection

2018 ◽  
Author(s):  
Adri Gabriel Sooai ◽  
Patrisius Batarius ◽  
Yovinia Carmeneja Hoar Siki ◽  
Paskalis Andrianus Nani ◽  
Natalia Magdalena Rafu Mamulak ◽  
...  
Keyword(s):  
Feature Selection ◽  
Recognition Accuracy ◽  
Hand Gesture

New Feature Selection Method Based on SVM-RFE

2014 ◽  
Vol 926-930 ◽  
pp. 3100-3104 ◽  
Author(s):  
Xi Wang ◽  
Qiang Li ◽  
Zhi Hong Xie
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Recognition Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Testing Time ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Accuracy Rate ◽  
New Feature

This article analyzed the defects of SVM-RFE feature selection algorithm, put forward new feature selection method combined SVM-RFE and PCA. Firstly, get the best feature subset through the method of cross validation of k based on SVM-RFE. Then, the PCA decreased the dimension of the feature subset and got the independent feature subset. The independent feature subset was the training and testing subset of SVM. Make experiments on five subsets of UCI, the results indicated that the training and testing time was shortened and the recognition accuracy rate of the SVM was higher.

NEAR-OPTIMAL GEOMETRIC FEATURE SELECTION FOR VISUAL SPEECH RECOGNITION

2013 ◽  
Vol 27 (08) ◽  
pp. 1350026 ◽  
Author(s):  
PREETY SINGH ◽  
VIJAY LAXMI ◽  
MANOJ SINGH GAUR
Keyword(s):  
Feature Selection ◽  
Speech Recognition ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Feature Reduction ◽  
Visual Speech ◽  
Visual Features ◽  
Geometric Feature ◽  
Large Set ◽  
Visual Speech Recognition

To improve the accuracy of visual speech recognition systems, selection of visual features is of fundamental importance. Prominent features, which are of maximum relevance for speech classification, need to be selected from a large set of extracted visual attributes. Existing methods apply feature reduction and selection techniques on image pixels constituting region-of-interest (ROI) to reduce data dimensionality. We propose application of feature selection methods on geometrical features to select the most dominant physical features. Two techniques, Minimum Redundancy Maximum Relevance (mRMR) and Correlation-based Feature Selection (CFS), have been applied on the extracted visual features. Experimental results show that recognition accuracy is not compromised when a few selected features from the complete visual feature set are used for classification, thereby reducing processing time and storage overheads considerably. Results are compared with performance of principal components obtained by application of Principal Component Analysis (PCA) on our dataset. Our set of selected features outperforms the PCA transformed data. Results show that the center and corner segments of the mouth are major contributors to visual speech recognition. Teeth pixels are shown to be a prominent visual cue. It is also seen that lip width contributes more towards visual speech recognition accuracy as compared to lip height.

Feature selection by genetic algorithms for mass spectral classifiers

2001 ◽  
Vol 446 (1-2) ◽  
pp. 483-492 ◽  
Author(s):  
H Yoshida ◽  
R Leardi ◽  
K Funatsu ◽  
K Varmuza
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Mass Spectral
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