Classification of Microcalcification Using Dual-Tree Complex Wavelet Transform and Support Vector Machine

2010 ◽  
Author(s):  
Andy Tirtajaya ◽  
Diaz D. Santika
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

scholarly journals Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

2016 ◽  
Vol 6 (6) ◽  
pp. 169 ◽  
Author(s):  
Shuihua Wang ◽  
Siyuan Lu ◽  
Zhengchao Dong ◽  
Jiquan Yang ◽  
Ming Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Complex Wavelet Transform ◽  
Pathological Brain Detection ◽  
Complex Wavelet

A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Shichang Du ◽  
Changping Liu ◽  
Lifeng Xi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Manufacturing Process ◽  
Classification Accuracy ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Complex Wavelet Transform ◽  
High Definition ◽  
Workpiece Surface ◽  
Surface Patterns

The surface appearance is sensitive to change in the manufacturing process and is one of the most important product quality characteristics. The classification of workpiece surface patterns is critical for quality control, because it can provide feedback on the manufacturing process. In this study, a novel classification approach for engineering surfaces is proposed by combining dual-tree complex wavelet transform (DT-CWT) and selective ensemble classifiers called modified matching pursuit optimization with multiclass support vector machines ensemble (MPO-SVME), which adopts support vector machine (SVM) as basic classifiers. The dual-tree wavelet transform is used to decompose three-dimensional (3D) workpiece surfaces, and the features of workpiece surface are extracted from wavelet sub-bands of each level. Then MPO-SVME is developed to classify different workpiece surfaces based on the extracted features and the performance of the proposed approach is evaluated by computing its classification accuracy. The performance of MPO-SVME is validated in case study, and the results demonstrate that MPO-SVME can increase the classification accuracy with only a handful of selected classifiers.

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