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Breast cancer is the most common malignant tumor for women. In the past twenty years, the incidence of breast cancer continues to rise. Then, the diagnosis and treatment of the breast cancer have become an extremely urgent work to do. In this study, we intend to build a diagnostic model of breast cancer by using data mining techniques. A feature selection method, INTERACT is applied to select relevant...
This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset...
This paper analyzes the concentration and dispersion of the integrated feature selection algorithm (TFFS),and finds their shortcomings: it is difficult for concentration to measure the weigh of the low frequent terms; dispersion ignores the impact of term whose mutual information is negative. Propose a modified feature selection algorithm (TFFSL), which makes certain improvements on concentration...
This paper presents an effective feature selection method for support vector machine (SVM). Unlike the traditional combinatorial searching method, feature selection is translated into the model selection of SVM which has been well studied. In more detail, the basic idea of this method is to tune the parameters of the Gaussian ARD (Automatic Relevance Determination) kernel via optimization of kernel...
Microarray is an important tool in gene analysis research. It can help identify genes that might cause various cancers. In this paper, we use feature selection methods and the support vector machine (SVM) to search for the disease-causing genes in microarray data of three different cancers. The feature selection methods are based on Euclidian distance (ED) and Pearson correlation coefficient(PCC)...
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