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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 1571–1580 of 1706 papers

TitleStatusHype
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks—0
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features—0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
Credit card fraud detection using machine learning: A survey—0
Cross-Class Relevance Learning for Temporal Concept Localization—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified