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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 1051–1075 of 1706 papers

TitleStatusHype
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow—0
CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering—0
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder—0
Incorporating Word Attention into Character-Based Word SegmentationCode0
Breast mass classification in ultrasound based on Kendall's shape manifold—0
Approximation Ratios of Graph Neural Networks for Combinatorial Problems—0
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Multiple perspectives HMM-based feature engineering for credit card fraud detectionCode0
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender SystemsCode0
Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewCode0
Selectivity Estimation for Range Predicates using Lightweight Models—0
Computing committor functions for the study of rare events using deep learning with importance sampling—0
Fake News Early Detection: An Interdisciplinary Study—0
Latent Variable Session-Based Recommendation—0
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data—0
DDGK: Learning Graph Representations for Deep Divergence Graph KernelsCode0
Predict Future Sales using Ensembled Random Forests—0
Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred EmbeddingsCode0
SynC: A Unified Framework for Generating Synthetic Population with Gaussian CopulaCode1
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognitionCode0
Multimodal Speech Emotion Recognition and Ambiguity ResolutionCode0
Feature Engineering for Mid-Price Prediction with Deep Learning—0
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement LearningCode0
A Graph-based Model for Joint Chinese Word Segmentation and Dependency ParsingCode0
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified