A Matlab Toolbox for Feature Importance Ranking
This provides a tool for researchers and practitioners in medical imaging to compare feature ranking methods, but it is incremental as it integrates existing algorithms without introducing new ones.
The authors developed a Matlab toolbox integrating 30 feature importance ranking algorithms and evaluated it on a database of 163 ultrasound images with 15 features per lesion, using linear SVM for malignancy prediction to analyze effectiveness through performance comparison.
More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR approaches 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 mass lesion, 15 features are extracted. To figure out the 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 online (https://github.com/NicoYuCN/matFIR). In our future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated.