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 401–450 of 1706 papers

TitleStatusHype
One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar ClassificationCode0
CLRGaze: Contrastive Learning of Representations for Eye Movement SignalsCode0
Deep Learning-Based Automatic Downbeat Tracking: A Brief ReviewCode0
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable BytesCode0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
PADME: A Deep Learning-based Framework for Drug-Target Interaction PredictionCode0
DeepTriangle: A Deep Learning Approach to Loss ReservingCode0
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression LearningCode0
Photometric identification of compact galaxies, stars and quasars using multiple neural networksCode0
Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data AugmentationCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
Principal Orthogonal Latent Components Analysis (POLCA Net)Code0
Probabilistic Bag-Of-Hyperlinks Model for Entity LinkingCode0
AutoLearn - Automated Feature Generation and SelectionCode0
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral FeaturesCode0
Quantifying yeast colony morphologies with feature engineering from time-lapse photographyCode0
Reconstruction of Incomplete Wildfire Data using Deep Generative ModelsCode0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty DetectionCode0
RELand: Risk Estimation of Landmines via Interpretable Invariant Risk MinimizationCode0
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction PredictionCode0
Auto deep learning for bioacoustic signalsCode0
Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical ImagingCode0
Repurposing recidivism models for forecasting police officer use of forceCode0
Advancing Automated Deception Detection: A Multimodal Approach to Feature Extraction and AnalysisCode0
DeepAtom: A Framework for Protein-Ligand Binding Affinity PredictionCode0
Complex Word Identification as a Sequence Labelling TaskCode0
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning ApproachCode0
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in textCode0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
Sentiment Analysis of Citations Using Word2vecCode0
Seoul bike trip duration prediction using data mining techniquesCode0
Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the OvenCode0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
DDGK: Learning Graph Representations for Deep Divergence Graph KernelsCode0
A Graph-based Model for Joint Chinese Word Segmentation and Dependency ParsingCode0
AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracyCode0
Danish Stance Classification and Rumour ResolutionCode0
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text ScoringCode0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworksCode0
Correlation of Object Detection Performance with Visual Saliency and Depth EstimationCode0
Deduplication Over Heterogeneous Attribute Types (D-HAT)Code0
Solving the "false positives" problem in fraud predictionCode0
Cross-lingual Knowledge Graph Alignment via Graph Convolutional NetworksCode0
Stock Movement Prediction from Tweets and Historical PricesCode0
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognitionCode0
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified