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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 51–75 of 1706 papers

TitleStatusHype
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Pushing the boundaries of molecular property prediction for drug discovery with multitask learning BERT enhanced by SMILES enumerationCode1
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
CASPR: Customer Activity Sequence-based Prediction and RepresentationCode1
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source LearningCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence UnderstandingCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Zero-shot hashtag segmentation for multilingual sentiment analysisCode1
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
Understanding the Dynamics of DNNs Using Graph ModularityCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge DevicesCode1
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the BoundaryCode1
Structural Characterization for Dialogue DisentanglementCode1
Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural modelCode1
Generative Pre-Training from MoleculesCode1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Sequence-to-Sequence Learning with Latent Neural GrammarsCode1
PTRAIL -- A python package for parallel trajectory data preprocessingCode1
Graph Contrastive Learning for Anomaly DetectionCode1
Establishing process-structure linkages using Generative Adversarial NetworksCode1
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space DecompositionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified