Jan 17, 2025, added a feature ranking algorithm {self_growing_forest}

Here shows matFR, a matlab toolbox for feature ranking

Background
  To collect tens of thousands of features is feasible in medical imaging and biology, and how to figure out these informative and discriminative ones becomes urgently important. The purpose of this project is to collect feature ranking (FR) methods, to provide a platform easy-to-use for potential researchers, and to call for designers online-sharing their previously or newly developed algorithms.

Methods
  The matFR toolbox has already integrated 42 methods. Among them, 12 methods are from FSLib [1], 9 methods from mutual information (MI) based feature selection repository [2], 7 methods are used in MATLAB (“rankfeatures”, “relieff” and “lasso”), and others are accessible online. FR methods can be grouped into supervised and unsupervised methods. In this toolbox, there are 29 supervised and 13 unsupervised methods. FR methods can also be categorized from theoretical perspective. The matFR contains 12 MI based methods, 8 statistical analysis based methods, 8 structure learning based methods and others.

An example
  Given an input matrix $X$ ($m$ instances and $n$ features per instance) and its corresponding labels $Y$ ($Y \in {0, 1}$), the procedure of using one ($f$) of the FR methods ($F$) in the toolbox can be described as $r = F(X, f, Y )$, where $r$ stands for the output rank indexes of features in a descending order with regard to the relative importance of features. If an unsupervised method is selected, the class labels $Y$ can be omitted.
  As shown in Figure under the folder "how2use", a user can activate any FR methods through an interface function ‘matFR_interface.m’ which determines the belonging of a method f. If $f$ is a MI based FR method, the function ‘matFR_mi.m’ is triggered, else the other function ‘matFR_fn.m’ is activated.
  An example is shown. Given the BCDR-F03 data set (bcdr_data_whole.mat), it contains 406 lesions and 736 mammographic images [3]. To each annotated image, 17 features are selected to quantify the lesions from mass intensity, contour shape and lesion texture. Among the mass lesions, 230 are histologically verified benign ($y = 0$) and 176 are malignant ($y = 1$). Thus, to the input matrix $X$, $m = 736$ and $n = 17$. After the data is prepared, one line code to activate the Laplacian Score algorithm [4], an unsupervised method, is shown as below,

r = matFR_interface ( X, ‘h2_fir_laplacian_score ’ );

and one line code to activate a joint MI based FR algorithm [5], a supervised method, is shown as below,

r = matFR_interface ( X, ‘b9_mi_joint ’ , Y).

Note that the short names of all FR methods are listed in the ‘demo.m’ file. As for further details, please refer to the publications and algorithm implementations.

Implementation
  The toolbox is implemented with MATLAB, except the MI based methods require a C++ compiler for two cpp files. One file is to compute the pairwise MI matrix between feature-feature and feature-class, and the other is for the joint MI matrix.
  For convenience, the files were compiled, and users can activate them without re-compilation if you deploy the toolbox on 64-bit Windows 7/10 systems. The toolbox has been successfully tested on the MATLAB R2015a and later versions. In addition, the cpp files were compiled by using MinGW64 Compiler (C++) and Microsoft Visual C++ 2012 successfully.

Future work
  The future work arises from three aspects. First, to integrate available FR methods into the toolbox and to follow up newly developed methods. The most promising way is contributions from algorithm developers to this project through online collaboration. Second, to complete the details of FR methods. For instance, some advanced discretization algorithms (CAIR [6] and CAIM [7]) could be adopted to MI based methods for diverse options. Last but not the least, to accelerate the distribution of these FR algorithms, the toolbox could be implemented in R and Python.

Reference
[1] Roffo, G. (2016) Feature selection library (MATLAB toolbox), arXiv preprint, arXiv, 1607.01327.
[2] Nguyen, X.V., Chan, J., Romano, S. and Bailey, J. (2014) Effective global approaches for mutual information based feature selection, 20th ACM SIGKDD international conference on knowledge discovery and data mining, 512-521.
[3] Arevalo, J., Gonzalez, F.A., Ramos-Pollan, R., Oliveira, J.L., Lopez, M.A.G. (2016) Representation learning for mammography mass lesion classification with convolutional neural networks, Computer methods and programs in biomedicine, 127, 248-257.
[4] He, X., Cai, D., Niyogi, P. (2006) Laplacian score for feature selection, Advances in neural information processing systems, 507-514.
[5] Nguyen, X.V., Chan, J., Romano, S. and Bailey, J. (2014) Effective global approaches for mutual information based feature selection, 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 512-521.
[6] Ching, J.Y., Wong, A.K.C., Chan, K.C.C. (1995) Class-dependent discretization for inductive learning from continuous and mixed-mode data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(7), 641-651.
[7] Kurgan, L.A., Cios, K.J. (2004) CAIM discretization algorithm, IEEE transactions on Knowledge and Data Engineering, 16(2), 145-153.

if finding the matFR toolbox useful, please refer to

Zhang Z, Liang X, Qin W, Yu S, Xie Y. matFR: a MATLAB toolbox for feature ranking. Bioinformatics. 2020 Oct 1; 36(19): 4968-4969.