Posts Tagged ‘ensemble’

Why “Many-Model Thinkers” Make Better Decisions

Saturday, November 24th, 2018

Why “Many-Model Thinkers” Make Better Decisions Intuitive description of #MachineLearning concepts. Focuses on practical business contexts (eg hiring) & explains how #ensemble models & boosting can make better choices

“The agent based model is not necessarily better. It’s value comes from focusing attention where the standard model does not.

The second guideline borrows the concept of boosting, …Rather than look for trees that predict with high accuracy in isolation, boosting looks for trees that perform well when the forest of current trees does not.

A boosting approach would take data from all past decisions and see where the first model failed. …The idea of boosting is to go searching for models that do best specifically when your other models fail.

To give a second example, several firms I have visited have hired computer scientists to apply techniques from artificial intelligence to identify past hiring mistakes. This is boosting in its purest form. Rather than try to use AI to simply beat their current hiring model, they use AI to build a second model that complements their current hiring model. They look for where their current model fails and build new models to complement it.”

PLOS Computational Biology: PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Monday, November 28th, 2016

PredictSNP…Consensus Classifier for Prediction of Disease-Related
Mutations Demo of various #ensemble approaches

PLOS Computational Biology: Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Sunday, August 31st, 2014

– apply to metabric consortium
– 17K clin feat. + ~50K gene exp. + ~30K CNVs ==to-predict==> 10yr survival – uses CI instead of AUC for real valued predictions
– combine collaboration & competition to beat the baseline (cox regression on only clinical features)
– mol. feat. on their own don’t work well due to the curse of dimensionality – features more important than the learning method

Pandey mentions: Cancer Survival Analysis through
Competition-Based…Modeling, using Human #Ensembles #kdd2014

IEEE Xplore Abstract – A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

Sunday, August 24th, 2014

Pandey mentions: Comparative Analysis of #Ensemble Classifiers [eg mean agg. or stacking]…in Genomics #kdd2014

performance-diversity tradeoff: should one incl. higher performance, lower diversity ones…. but still adding diversity is good

related to

Ensemble Methods in Machine Learning. Proceedings of the First International Workshop on Multiple Classifier Systems

Sunday, July 13th, 2014

Rich C, Alexandru N-M, Geoff C, Alex K (2004) Ensemble selection from libraries of
models. Proceedings of the twenty-first international conference on Machine learning. Banff, Alberta, Canada: ACM.

Thomas GD (2000) Ensemble Methods in Machine Learning. Proceedings of the First International Workshop on Multiple Classifier Systems: Springer-Verlag.

.@deniseOme Good ref is TG Dietterich #Ensemble Methods in
#MachineLearning MCS ’00 Not rel. to @ensembl #ismb #afp14

ref 17 & 18