One of the major challenges of making the Relief-based algorithms of ReBATE flexible enough to handle different dataset types, i.e. (1) continuous, discrete, or mixed feature types, (2) binary, multiclass, or continuous outcomes, (3) presence of missing data, is to do so in a way that preserves computational efficiency. Presently scikit-rebate is implemented in a fairly compact manner, however this may not ultimately be the most efficient implementation. This issue posting seeks enhancements to ReBATE and it's underlying algorithms (i.e. ReliefF, SURF, SURF*, MultiSURF, MultiSURF*, TuRF) to make the respective algorithms run faster, and utilize less memory.
One of the major challenges of making the Relief-based algorithms of ReBATE flexible enough to handle different dataset types, i.e. (1) continuous, discrete, or mixed feature types, (2) binary, multiclass, or continuous outcomes, (3) presence of missing data, is to do so in a way that preserves computational efficiency. Presently scikit-rebate is implemented in a fairly compact manner, however this may not ultimately be the most efficient implementation. This issue posting seeks enhancements to ReBATE and it's underlying algorithms (i.e. ReliefF, SURF, SURF*, MultiSURF, MultiSURF*, TuRF) to make the respective algorithms run faster, and utilize less memory.