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Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 676–700 of 1706 papers

TitleStatusHype
Towards Personalized and Human-in-the-Loop Document Summarization—0
Data-driven Smart Ponzi Scheme Detection—0
Graph Contrastive Learning for Anomaly DetectionCode1
Feature Engineering with Regularity StructuresCode0
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Classification of Electrical Impedance Tomography Data Using Machine Learning—0
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering—0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction—0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection—0
LocalGLMnet: interpretable deep learning for tabular data—0
Establishing process-structure linkages using Generative Adversarial NetworksCode1
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space DecompositionCode1
Residual Attention Based Network for Automatic Classification of Phonation Modes—0
Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based EnsemblesCode1
Feature Cross Search via Submodular Optimization—0
NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC—0
A Data-Driven Method for Recognizing Automated Negotiation Strategies—0
Free-Text Keystroke Dynamics for User Authentication—0
Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified