Exploring the limits of an RBSC-based approach in solving the subset selection problem

This study focuses on the subset selection problem of computational statistics and de- ploys rank-biserial correlation (RBSC) based deck generation algorithm (RBSC-SubGen) [1] in solving it. RBSC-SubGen is originally designed for automatically building a desired number vocabulary decks (out large corpus) with level word frequency relation, which shares many common aspects generic problem. In this article, we consider applying it not only corpora but any set ranked items its resilience against various hyper-parameters, are treated previ- ous studies. Namely, simulations test under constraints indicate vulnerable terms rate saturation, cost accuracy obtained solution.