matFR: a MATLAB toolbox for feature ranking

Author:

Zhang Zhicheng12,Liang Xiaokun1,Qin Wenjian1,Yu Shaode345ORCID,Xie Yaoqin1

Affiliation:

1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, GD 518055, China

2. Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA

3. College of Information and Communication Engineering, Communication University of China, Beijing 100024, China

4. Key Laboratory of Convergent Media and Intelligent Technology (Communication University of China), Ministry of Education, Beijing 100024, China

5. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

Abstract

Abstract Summary Nowadays, it is feasible to collect massive features for quantitative representation and precision medicine, and thus, automatic ranking to figure out the most informative and discriminative ones becomes increasingly important. To address this issue, 42 feature ranking (FR) methods are integrated to form a MATLAB toolbox (matFR). The methods apply mutual information, statistical analysis, structure clustering and other principles to estimate the relative importance of features in specific measure spaces. Specifically, these methods are summarized, and an example shows how to apply a FR method to sort mammographic breast lesion features. The toolbox is easy to use and flexible to integrate additional methods. Importantly, it provides a tool to compare, investigate and interpret the features selected for various applications. Availability and implementation The toolbox is freely available at http://github.com/NicoYuCN/matFR. A tutorial and an example with a dataset are provided.

Funder

Shenzhen Matching Project

National Key Research and Develop Program of China

National Natural Science Foundation of China

Leading Talent of Special Support Project in Guangdong

Science Foundation of Guangdong

CAS Key Laboratory of Health Informatics

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference17 articles.

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3. Radiomics: images are more than pictures, they are data;Gillies;Radiology,2016

4. Dependence guided unsupervised feature selection;Guo;AAAI Conference on Artificial Intelligence, AAAI, California,2018

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