Nantong University

Tingzhen Qin

Yi Zhang

Ming Li

Weiping Ding

Ying Sun

Hengrong Ju

Haipeng Wang

Jialu Ding

Jiashuang Huang

Yu Geng

Xinjie Shen

Chun Cheng

A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.

Method for classifying multi-granularity breast cancer genes based on double self-adaptive neighborhood radius

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Nantong, China

Tingzhen Qin