Aims: Find a way to reduce the dimensionality of the dataset.
Background: Dimensionality reduction is the key issue of the machine learning process. It
doesn’t only improve the prediction performance, but also could recommend the intrinsic
features and help to explore the biological expression of the machine learning “black box”.
Objective: A variety of feature selection algorithms are used to select data features to
achieve dimensionality reduction.
Methods: First, MRMD2.0 integrated 7 different popular feature ranking algorithms with
PageRank strategy. Second, optimized dimensionality was detected with forward adding
Result: We have achieved good results in our experiments.
Conclusion: Several works have been tested with MRMD2.0. It showed the well performance.
Otherwise, it also can draw the performance curves according to the feature dimensionality.
If users want to sacrifice accuracy for fewer features, they can select the dimensionality from
the performance curves.
Other: We developed friendly python tools together with web server. The users could upload
their csv, arff or libsvm format files. Then the web server would help to rank features and
find the optimized dimensionality.