Data Feature Dimensionality Research Articles

A Novel Feature Selection Technique for Improved Survivability Diagnosis of Breast Cancer

Abstract In this paper we propose a novel Shapely Value Embedded Genetic Algorithm, called as SVEGA that improves the breast cancer diagnosis accuracy that selects the gene subset from the high dimensional gene data. Particularly, the embedded Shapely Value includes two memetic operators namely “include” and “remove” features (or genes) to realize the genetic algorithm (GA) solution. The method is ranking the genes according to its capability to differentiate the classes. The method selects the genes that can maximize the capability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Four classifiers such as Support vector machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and J48 are used on the breast cancer dataset from the Kent ridge biomedical repository to classify between the normal and abnormal tissues and to diagnose as benign and malignant tumours. The obtained classification accuracy demonstrates that the proposed method contributes to the superior diagnosis of breast cancer than the existing methods.

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

This paper presents a method for breast cancer diagnosis in digital mammogram images. Multiresolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.