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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 1706 papers

TitleStatusHype
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Energy-based Models for Video Anomaly Detection—0
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach—0
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory—0
Bidirectional LSTM for Named Entity Recognition in Twitter Messages—0
Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data—0
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach—0
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble—0
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach—0
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation—0
Enhancing Generalizability of Predictive Models with Synergy of Data and Physics—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Enhancing Physics-Informed Neural Networks Through Feature Engineering—0
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy—0
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—0
Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons—0
Ensemble learning of diffractive optical networks—0
Automated Essay Scoring with Discourse-Aware Neural Models—0
Show:102550
← PrevPage 27 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified