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
GraphCNNpred: A stock market indices prediction using a Graph based deep learning system—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
The Remarkable Robustness of LLMs: Stages of Inference?Code1
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning BenchmarksCode4
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Horseshoe-type Priors for Independent Component Estimation—0
LightGBM robust optimization algorithm based on topological data analysis—0
PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation ModelCode0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil—0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy—0
Learned Feature Importance Scores for Automated Feature Engineering—0
Dynamic and Adaptive Feature Generation with LLM—0
DeepMol: An Automated Machine and Deep Learning Framework for Computational ChemistrCode2
Iterative Feature Boosting for Explainable Speech Emotion RecognitionCode0
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationCode1
Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking SequencesCode1
Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction—0
Transitional Uncertainty with Layered Intermediate Predictions—0
Maintaining and Managing Road Quality:Using MLP and DNN—0
Wearable-based behaviour interpolation for semi-supervised human activity recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified