's are real-valued weights depending on the accuracy of h

In particular, interpreting Random Forests (RFs) [2] and its variants [14, 28, 27, 29, 1, 12] has become an important area of research due to the wide ranging applications of RFs invarious scientific areas, such asgenome-wide association studies (GWAS)[7],gene expression microarray[13,23],andgeneregulatorynetworks[9].

Thank you for all the reviewers time and effort. Thank you for your detailed review. Here, the idea is to re-train our model when new data is available. Here we explain our design space (see additional details in Appendix A.3, B and C); (i) Choice of embedding (joint vs Reviewer 3 Thank you for your review, and for comments regarding experiments, please see above. Thank you for your positive comments regarding the quality of the paper.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass.

The prediction concatenation (PreConc) operation is crucial for the multi-layer feature transformation in deep forest, though little has been known about its theoretical property.