Yes, I have been using the sklearn random forest and other ensemble methods at SigmaLabsInc. I can't tell you too much because it is proprietary, but we downsample the normal pixels some, upsample the anomalies some, and apply class weights to complete the balance.
We wrote a white paper that is available here https://sigmalabsinc.com/machine-learning-a-game-changer-for-additive-manufacturing/ On Sun, Aug 29, 2021 at 2:08 PM Jon Zingale <jonzing...@gmail.com> wrote: > I am presently working on learning weighted ensemble > <https://arxiv.org/pdf/1906.00856.pdf> sampling techniques and was > curious if any here have worked with them before. The technique seems > promising and has enjoyed quite a bit of success (even above MCMC > <https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo>) in circles > concerned with reaction rates for rare events. > > Some points of interest for me include: > > 1. A better sampling of fringe-outlier works/art from streaming > services. > 2. An alternative (bin-based sampling) to globally defined "fitness" > measures in evolutionary modeling. > 3. An application of diffusion-limited aggregation to general search > (especially in the face of limited resources) > 4. An application of linear logic to optimization problems in conformation > prediction <https://en.wikipedia.org/wiki/Protein_structure_prediction> > . > 5. Investigation of dynamical properties, such as distribution of > trajectories with "high winding number", on strange attractors. > > > While I am just beginning to grok the technique, I thought it might be > fruitful to ask here. > > Jon > - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . > FRIAM Applied Complexity Group listserv > Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam > un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com > FRIAM-COMIC http://friam-comic.blogspot.com/ > archives: http://friam.471366.n2.nabble.com/ >
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