Feature selected based on PCA and optimized LMC

In this article, we propose an optimization algorithm for the original LMC [1] (Large Margin Classifier). We use PCA [2] (Principal Component Analysis) to reduce the dimensionality of the images, and then put the data after dimensionality reduction into the optimized LMC for the feature selection [3]. We will get several features with the greatest distinction. We use these features to classify images. Finally, the experiment shows that the accuracy of the optimized LMC under the same dimensions is higher than that of the original LMC, and in many cases, the accuracy of the optimized LMC after taking 6 feature vectors has exceeded the highest accuracy of the original LMC.

[1]  Peter Kapec,et al.  Decision Support in Medical Data Using 3D Decision Tree Visualisation , 2019, 2019 E-Health and Bioengineering Conference (EHB).

[2]  Ravinder Nath,et al.  Comparison of PCA and 2D-PCA on Indian Faces , 2014, 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014).

[3]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[4]  Yongyi Yang,et al.  Improving SVM classifier with prior knowledge in microcalcification detection1 , 2012, 2012 19th IEEE International Conference on Image Processing.

[5]  Xiaoming Liu,et al.  Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method , 2014, IEEE Systems Journal.

[6]  Yang Ou,et al.  The Ship Collision Accidents Based on Logistic Regression and Big Data , 2019, 2019 Chinese Control And Decision Conference (CCDC).

[7]  Hakan Cevikalp,et al.  Large margin classifiers based on affine hulls , 2010, Neurocomputing.