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 1451–1500 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
AutoML-GPT: Large Language Model for AutoML—0
autoNLP: NLP Feature Recommendations for Text Analytics Applications—0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model—0
Horseshoe-type Priors for Independent Component Estimation—0
Bayesian Kernel Methods for Natural Language Processing—0
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty—0
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs—0
Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
BertAA : BERT fine-tuning for Authorship Attribution—0
BERTMap: A BERT-based Ontology Alignment System—0
Better Model Selection with a new Definition of Feature Importance—0
Beyond Context: A New Perspective for Word Embeddings—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text—0
Bidirectional LSTM for Named Entity Recognition in Twitter Messages—0
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons—0
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified