Live briefing
Everything about Feature engineering
Extracting features from raw data for machine learning
In supervised machine learning and statistical modeling, feature engineering is a preprocessing step which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant information, feature engineering significantly enhances their predictive accuracy and decision-making capability. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Feature engineeringA 5-minute orientation
- Skim a recent paper: Adaptive feature selection with shapley and hypothetical testing: Case study of EEG feature engineeringInfluential work since 2022
- Pick up a classic book: The First Snap-fit HandbookThe most-reprinted book on the subject
- Read the landmark paper: Feature-oriented product line engineeringCited 350 times
- Go sideways: Non-negative matrix factorizationA closely related topic
- Go sideways: Automated machine learningA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Feature-oriented product line engineering
A conceptual basis for feature engineering
Special issue on feature engineering editorial
Feature-based reverse engineering of mechanical parts
Dynamic Malware Analysis with Feature Engineering and Feature Learning
Taxonomy-aware feature engineering for microbiome classification
Associative feature modeling for concurrent engineering integration
Automated feature engineering for algorithmic fairness
🆕 Influential since 2022
Adaptive feature selection with shapley and hypothetical testing: Case study of EEG feature engineering
Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification
Feature engineering to detect fraud using healthcare claims data
Feature-based detection of breast cancer using convolutional neural network and feature engineering
Kinematic variables and feature engineering for particle phenomenology
📚 Books worth reading
The First Snap-fit HandbookPaul R. Bonenberger · 2000
Innovation in product designMonica Bordegoni, Caterina Rizzi · 2011
Designing software product lines with UMLHassan Gomaa · 2004
Dimensioning and Tolerancing HandbookPaul J. Drake · 1999- 📕Turn your computer into a money machineAvery Breyer · 2015
- 📕The ghost and the darknessStephen Hopkins, A. Kitman Ho · 2010
- 📕Vehicle design in relation to injuryConference on Safety in Motoring, London 1964 · 1964
Formal Foundations of Reuse and Domain EngineeringStephen H. Edwards · 2009
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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