Computer Studies Exploratory_Analysis_of_Feature_Selectio.pdf

Feature selection and feature extraction method were

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Retrieval as a technique that utilizes the visual content of an image to search for similar images in large scale image databases. Feature selection and feature extraction method were the significant tasks that were considered in image retrieval process [16]. Huanzhang et al. discussed about Feature subset selection as a significant subject when training classifiers in Machine Learning (ML) problems and illustrated the information that the complexity of the classifier parameters adjustment during training swells exponentially with the number of features. So they introduced a novel embedded feature selection method, called ESFS, which was simulated from the wrapper method SFS as it relies on the simple standard to add incrementally most relevant features [17]. Georgia et al., discussed the study of investigated information theoretic approach to feature selection for computer-aided diagnosis, the approach was based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature of their underlying relationships. They described MI that it can potentially offer some advantages over feature selection techniques that focus only on the linear relationships of variables [18]. Mohamed et al., discussed an approach which was proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs and showed the efficiency of feature selection on the CAD system, and implemented the proposed method in four stages which are [19]: a) The region of interest (ROI) selection of 32x32 pixels size which identifies clusters of microcalcifications, b) The feature extraction stage based on the wavelet decomposition of locally processed image (region of interest) to compute the significant features of each cluster, c) The feature selection stage, which select the most significant features to be used in next stage, and d) The classification stage, which classify between normal and microcalcifications' patterns and then classify between benign and malignant microcalcifications. Guo-Zheng et al.discussed the feature selection methods with support vector machines which contains obtained satisfactory results, and propose a prediction risk based on feature selection method with multiple classification support vector machines. The performance of the projected method is compared with the earlier methods of optimal brain damage rooted feature selection methods with binary support vector machines [4]. Shuqin et al., said feature selection techniques has been widely used in various fields and discussed a new refined feature selection module which utilizes two-step selection method in computer-aided diagnosis (CAD) system for liver disease, the method used was filter and wrapper method, Support Vector Machine (SVM) and Genetic Algorithm (GA) And stated that the advantage was to show the ability of accommodating multi feature selection search strategies and combining filter and wrapper method, especially in identifying optimal and minimal feature subsets for building the classifier [20]. Yong and Ding-gang described
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