A Feature Selection Based on Perturbation Theory

26 Feb 2019Javad Rahimipour AnarakiHamid Usefi

Consider a supervised dataset $D=[A\mid \textbf{b}]$, where $\textbf{b}$ is the outcome column, rows of $D$ correspond to observations, and columns of $A$ are the features of the dataset. A central problem in machine learning and pattern recognition is to select the most important features from $D$ to be able to predict the outcome... (read more)

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