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 1326–1350 of 1706 papers

TitleStatusHype
Multi-View Feature Engineering and Learning—0
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping—0
NCSU-SAS-Ning: Candidate Generation and Feature Engineering for Supervised Lexical Normalization—0
Neighborhood Adaptive Estimators for Causal Inference under Network Interference—0
NER for Medical Entities in Twitter using Sequence to Sequence Neural Networks—0
Network Embedding via Deep Prediction Model—0
NEUDM: A System for Topic-Based Message Polarity Classification—0
Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem—0
Neural Architectures for Biological Inter-Sentence Relation Extraction—0
Neural Automated Essay Scoring Incorporating Handcrafted Features—0
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing—0
Neural Feature Learning From Relational Database—0
Neural Math Word Problem Solver with Reinforcement Learning—0
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms—0
Neural Network Architectures for Arabic Dialect Identification—0
Neural Network for Heterogeneous Annotations—0
Neural Networks for Negation Cue Detection in Chinese—0
Neural Networks Leverage Corpus-wide Information for Part-of-speech Tagging—0
Hybrid Neural Tagging Model for Open Relation Extraction—0
Neural Recovery Machine for Chinese Dropped Pronoun—0
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)—0
NeurIPS 2024 Ariel Data Challenge: Characterisation of Exoplanetary Atmospheres Using a Data-Centric Approach—0
Neurology-as-a-Service for the Developing World—0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified