TY - GEN
T1 - A Matlab Toolbox for Feature Importance Ranking
AU - Yu, Shaode
AU - Zhang, Zhicheng
AU - Liang, Xiaokun
AU - Wu, Junjie
AU - Zhang, Erlei
AU - Qin, Wenjian
AU - Xie, Yaoqin
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to thank the editor and anonymous reviewers for the invaluable comments that improve the paper quality. This work is supported in part by grants from the Shenzhen matching project (GJHS20170314155751703), the National Natural Science Foundation of China (61871374), the National Key Research and Develop Program of China (2016YFC0105102), the Leading Talent of Special Support Project in Guangdong (2016TX03R139), the Fundamental Research Program of Shenzhen (JCYJ20170413162458312), the Science Foundation of Guangdong (2017B020229002, 2015B020233011, 2014A030312006) and the CAS Key Lab of Human-Machine Intelligence-Synergy Systems, Shenzhen Engineering Laboratory for Key Technologies on Intervention Diagnosis and Treatment Integration.
Funding Information:
The authors would like to thank the editor and anonymous reviewers for the invaluable comments that improve the paper quality. This work is supported in part by grants from the Shenzhen matching project (GJHS20170314155751703), the National Natural Science Foundation of China (61871374), the National Key Research and Develop Program of China (2016YFC0105102), the Leading Talent of Special Support Project in Guangdong (2016TX03R139), the Fundamental Research Program of Shenzhen (JCYJ20170413162458312), the Science Foundation of Guangdong (2017B020229002, 2015B020233011, 2014A030312006) and the CAS Key Lab of Human-Machine Intelligence-Synergy Systems, Shenzhen Engineering Laboratory for Key Technologies on Intervention Diagnosis and Treatment Integration.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - More attention is paid for the feature importance ranking (FIR), in particular when high-throughput features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR methods have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast lesion, 15 features are handcrafted. And to Figure out an optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is available (https://github.com/NicoYuCN/matFIR). In the future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated.
AB - More attention is paid for the feature importance ranking (FIR), in particular when high-throughput features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR methods have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast lesion, 15 features are handcrafted. And to Figure out an optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is available (https://github.com/NicoYuCN/matFIR). In the future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated.
KW - feature importance ranking
KW - feature selection
KW - intelligent diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85085998593&partnerID=8YFLogxK
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U2 - 10.1109/ICMIPE47306.2019.9098233
DO - 10.1109/ICMIPE47306.2019.9098233
M3 - Conference contribution
AN - SCOPUS:85085998593
T3 - 2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019
BT - 2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019
Y2 - 22 November 2019 through 24 November 2019
ER -