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 1401–1425 of 1706 papers

TitleStatusHype
Arabic Named Entity Recognition: What Works and What's Next—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification—0
Are Accelerometers for Activity Recognition a Dead-end?—0
A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts—0
A Review of Computational Approaches for Evaluation of Rehabilitation Exercises—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks—0
Article citation study: Context enhanced citation sentiment detection—0
Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions—0
Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Health—0
Artificial Intelligence Technology analysis using Artificial Intelligence patent through Deep Learning model and vector space model—0
Artificial Neural Networks for Finger Vein Recognition: A Survey—0
A Sentence Interaction Network for Modeling Dependence between Sentences—0
A Simple and Effective Approach to the Story Cloze Test—0
A Simple and Effective Dependency Parser for Telugu—0
A simple framework for contrastive learning phases of matter—0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting—0
ASM Kernel: Graph Kernel using Approximate Subgraph Matching for Relation Extraction—0
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading—0
Asset price movement prediction using empirical mode decomposition and Gaussian mixture models—0
Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified