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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 401–425 of 1706 papers

TitleStatusHype
CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations—0
ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
Clustering US Counties to Find Patterns Related to the COVID-19 Pandemic—0
CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning—0
CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter—0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification—0
CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering—0
CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings—0
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches—0
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT—0
Combining Lexical and Semantic-based Features for Answer Sentence Selection—0
Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures—0
Combining Tree Structures, Flat Features and Patterns for Biomedical Relation Extraction—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Comparative Analysis of Machine Learning and Deep Learning Algorithms for Detection of Online Hate Speech—0
Comparative Performance of Machine Learning Algorithms for Early Genetic Disorder and Subclass Classification—0
Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified