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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 1076–1100 of 1706 papers

TitleStatusHype
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
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
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
The Landscape of R Packages for Automated Exploratory Data AnalysisCode0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
SAFE ML: Surrogate Assisted Feature Extraction for Model LearningCode0
Field-aware Neural Factorization Machine for Click-Through Rate Prediction—0
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading—0
Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning—0
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty DetectionCode0
Machine learning and chord based feature engineering for genre prediction in popular Brazilian musicCode0
The Spatially-Conscious Machine Learning Model—0
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side InformationCode0
Flexible Operator Embeddings via Deep Learning—0
Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification—0
The autofeat Python Library for Automated Feature Engineering and SelectionCode0
A Comparative Analysis of Android Malware—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified