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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 376–400 of 1706 papers

TitleStatusHype
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Chronic Diseases Prediction Using ML—0
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space—0
Citcom – Citation Recommendation—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Classification of Electrical Impedance Tomography Data Using Machine Learning—0
Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model—0
Classification of residential and non-residential buildings based on satellite data using deep learning—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Classifying Malware Using Function Representations in a Static Call Graph—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Classifying single-qubit noise using machine learning—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Arabic Named Entity Recognition: What Works and What's Next—0
Clickbait detection using word embeddings—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Clinical Event Detection with Hybrid Neural Architecture—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified