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 576–600 of 1706 papers

TitleStatusHype
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Neighborhood Adaptive Estimators for Causal Inference under Network Interference—0
Intent Recognition in Conversational Recommender Systems—0
Feature Selection with Distance Correlation—0
Novel Modelling Strategies for High-frequency Stock Trading Data—0
Deduplication Over Heterogeneous Attribute Types (D-HAT)Code0
Photometric identification of compact galaxies, stars and quasars using multiple neural networksCode0
Small Language Models for Tabular DataCode0
A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks—0
Feature Engineering vs BERT on Twitter Data—0
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks—0
Automatic Seizure Prediction using CNN and LSTM—0
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—0
Feature Engineering and Classification Models for Partial Discharge in Power Transformers—0
Machine Learning for K-adaptability in Two-stage Robust OptimizationCode0
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Object-Category Aware Reinforcement Learning—0
Less is More: Facial Landmarks can Recognize a Spontaneous SmileCode0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
Point Cloud Recognition with Position-to-Structure Attention Transformers—0
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified