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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 501–525 of 1706 papers

TitleStatusHype
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
A neural network model for solvency calculations in life insurance—0
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry—0
Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction—0
AutoML-GPT: Large Language Model for AutoML—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
A Cognition Based Attention Model for Sentiment Analysis—0
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
AutoML for Contextual Bandits—0
A Neural Network Based Explainable Recommender System—0
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
Automation of Feature Engineering for IoT Analytics—0
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
Automating Feature Engineering—0
Automatic Seizure Prediction using CNN and LSTM—0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
An Error Analysis Tool for Natural Language Processing and Applied Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified