In the seminal paper “Gene Selection for Cancer Classification using Support Vector Machines“, Guyon, Weston, Barnhill, and Vapnik (2002) use Recursive Feature Elimination to find the genes which are the most predictive of cancer. Recursive Feature Elimination repeatedly ranks the features and eliminates the worst feature until only a small subset of the original set of features remains. Although several feature ranking methods were explored, the main method was a soft margin SVM classifier with which the authors found 8 key colon cancer genes out of 7000.

Related Posts via Categories

• AlphaGo is changing how the Game is Played
• Counterfactual Regret Minimization
• Richardson Extrapolation
• University of Alberta Solves Poker
• 179 classifiers competing on 121 data sets and the winner is …
• A review of “Playing Atari with Deep Reinforcement Learning”
• A Review of “Knowledge Vault: A Web-Scale Approach to a Probabilistic Knowledge Fusion”
• Rules for CS Reseach and Seven Principles of Learning
• DeepLearning.net’s Reading List
• Gunnar Carlsson on the Shape of Data