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 1001–1050 of 1706 papers

TitleStatusHype
Reusable workflows for gender prediction—0
Reuse and Adaptation for Entity Resolution through Transfer Learning—0
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis—0
Review of automated time series forecasting pipelines—0
Revisiting Character-Based Neural Machine Translation with Capacity and Compression—0
Revisiting the Role of Feature Engineering for Compound Type Identification in Sanskrit—0
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
Robust cross-domain disfluency detection with pattern match networks—0
Robust Domain Adaptation for Relation Extraction via Clustering Consistency—0
Robust Event Classification Using Imperfect Real-world PMU Data—0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
Robust PDF Document Conversion Using Recurrent Neural Networks—0
Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings—0
Robust Tracking Using Region Proposal Networks—0
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts—0
Role of Morpho-Syntactic Features in Estonian Proficiency Classification—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation—0
Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training—0
Rule-based vs. Neural Net Approaches to Semantic Textual Similarity—0
RUL forecasting for wind turbine predictive maintenance based on deep learning—0
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering—0
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks—0
Sales Forecast in E-commerce using Convolutional Neural Network—0
Sample-Efficient Behavior Cloning Using General Domain Knowledge—0
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks—0
SA-UZH: Verb-based Sentiment Analysis—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable Deployment of AI Time-series Models for IoT—0
Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency—0
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces—0
Schooling to Exploit Foolish Contracts—0
Scientific Computing with Diffractive Optical Neural Networks—0
SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification—0
Scoring Root Necrosis in Cassava Using Semantic Segmentation—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—0
Segmentation of Argumentative Texts with Contextualised Word Representations—0
Selectivity Estimation for Range Predicates using Lightweight Models—0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Self-Optimizing Feature Transformation—0
Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design—0
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Semantic Annotation for Tabular Data—0
Semantic Frame Labeling with Target-based Neural Model—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection—0
Semantic Loss Application to Entity Relation Recognition—0
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified