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Everything about Feature selection

Process in machine learning and statistics

In machine learning, feature selection is the process of selecting a subset of relevant features for use in model construction. Feature selection techniques are used for several reasons:simplification of models to make them easier to interpret, shorter training times, to avoid the curse of dimensionality, improve the compatibility of the data with a certain learning model class, to encode inherent symmetries present in the input space. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Feature selectionA 5-minute orientation
  2. Skim a recent paper: A Review of Feature Selection Methods for Machine Learning-Based Disease Risk PredictionInfluential work since 2022
  3. Pick up a classic book: Distinctive feature theoryThe most-reprinted book on the subject
  4. Read the landmark paper: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancyCited 9,140 times
  5. Go sideways: Matrix regularizationA closely related topic
  6. Go sideways: Regularized least squaresA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Introduction
  2. Subset selection
  3. Optimality criteria
  4. Structure learning
  5. Information theory-based feature selection mechanisms
  6. Hilbert-Schmidt Independence Criterion Lasso based feature selection
  7. Correlation feature selection
  8. Regularized trees
  9. Overview on metaheuristics methods
  10. Feature selection embedded in learning algorithms

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics