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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 1451–1475 of 1706 papers

TitleStatusHype
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding—0
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way—0
Automated Essay Scoring with Discourse-Aware Neural Models—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques—0
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods—0
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks—0
Automatic Feature Engineering for Answer Selection and Extraction—0
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion—0
Automatic Features for Essay Scoring -- An Empirical Study—0
Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
Automatic Seizure Prediction using CNN and LSTM—0
Automating Feature Engineering—0
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques—0
Automation of Feature Engineering for IoT Analytics—0
AutoML for Contextual Bandits—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified