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Future work on FastRF
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Systematically investigate the effects of the parameters for
m_Kvalue(number of features considered in a node) andm_numFeatTree(number of features considered in a tree) on a larger number of datasets. Propose better default values. -
Add support numeric class attributes (regression trees)
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Change the one-vs-all binary split used for categorical attributes to a multifurcating split, as in Weka RF. This will improve the execution time on datasets with many multi-level categorical attributes.
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Test the algorithm to calculate dropout feature importance - how it relates to standard feature importance measures?
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Benchmark FastRF on many-core machines. Compare to other popular non-Java RF implementations.
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Make FastRF into a Weka package, therefore easier to install into the Weka GUI.
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Write a wrapper for R.
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... we'd be happy to hear your suggestions!