Publications

@misc{Nordling2022ATAT,

title = {Feature Selection under Uncertainty–Avoiding the Combinatorial Explosion by Making the Problem Seemingly Harder},

author = {Torbjörn E. M. Nordling},

url = {https://sites.google.com/site/atathpsc/},

year  = {2022},

date = {2022-03-01},

booktitle = {Conference on Advance Topics and Auto Tuning in High-Performance Scientific Computing},

pages = {0},

publisher = {Dept. of Math., National Cheng Kung University (NCKU), Tainan City, Taiwan},

address = {No. 1 University Rd., 701 Tainan, Taiwan (R.O.C.)},

abstract = {Feature selection is a challenging fundamental problem encountered every time one constructs a model of a system. Typically prior knowledge on which features that are essential to measure and include is used. In lack of such knowledge, various feature selection methods based on trying different feature combinations, ranging from exhaustive testing of every possible combination of features to various heuristic search algorithms, are used. In this presentation, I discuss feature selection under uncertainty caused by measurement noise in a linear regression problem. I demonstrate that despite the noise seemingly making the feature selection harder, it can be used to estimate uncertainty sets of each feature and convert the problem into a feature selection under uncertainty problem for which a provably necessary subset of features can be found. To encourage discussion and hopefully provide intuitive understanding this is all presented using one single example motivated by the need to infer gene regulatory networks.},

howpublished = {Conference on Advance Topics and Auto Tuning in High-Performance Scientific Computing},

keywords = {Feature selection},

pubstate = {published},

tppubtype = {misc}

}