SOTAVerified

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 1026–1050 of 1706 papers

TitleStatusHype
HONEM: Learning Embedding for Higher Order Networks—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
Anomaly Detection in High Dimensional DataCode0
A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management—0
Metapath-guided Heterogeneous Graph Neural Network for Intent RecommendationCode0
Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes—0
Responsive and Self-Expressive Dialogue GenerationCode0
Automated Essay Scoring with Discourse-Aware Neural Models—0
Segmentation of Argumentative Texts with Contextualised Word Representations—0
Arabic Named Entity Recognition: What Works and What's Next—0
ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification—0
sql4ml A declarative end-to-end workflow for machine learningCode0
Supervised and Unsupervised Neural Approaches to Text ReadabilityCode0
Hybrid Neural Tagging Model for Open Relation Extraction—0
The Effect of Visual Design in Image Classification—0
Techniques for Automated Machine Learning—0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment—0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
Deep Neural Baselines for Computational Paralinguistics—0
Encoding high-cardinality string categorical variablesCode0
An Enhanced Ad Event-Prediction Method Based on Feature Engineering—0
Danish Stance Classification and Rumour ResolutionCode0
Complex Word Identification as a Sequence Labelling TaskCode0
Multilingual and Multitarget Hate Speech Detection in Tweets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified