PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB

BackgroundOne of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research.ResultsTo meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.ConclusionPROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.

[1]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[2]  Adam Godzik,et al.  Improving the chances of successful protein structure determination with a random forest classifier. , 2014, Acta crystallographica. Section D, Biological crystallography.

[3]  Shuiwang Ji,et al.  SLEP: Sparse Learning with Efficient Projections , 2011 .

[4]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.