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 226–250 of 1706 papers

TitleStatusHype
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification—0
A State-of-the-Art Mention-Pair Model for Coreference Resolution—0
A streamable large-scale clinical EEG dataset for Deep Learning—0
A strong baseline for question relevancy ranking—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code—0
Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Approximation Ratios of Graph Neural Networks for Combinatorial Problems—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
A Survey on Churn Analysis—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
A Survey on Semantics in Automated Data Science—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
A System for Diacritizing Four Varieties of Arabic—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting—0
A Time-Frequency based Suspicious Activity Detection for Anti-Money Laundering—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction—0
Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified