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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 1576–1600 of 1706 papers

TitleStatusHype
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
Cross-lingual Short-text Matching with Deep Learning—0
Democratizing AI: Non-expert design of prediction tasks—0
CTSys at SemEval-2018 Task 3: Irony in Tweets—0
Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network—0
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models—0
Customers Churn Prediction in Financial Institution Using Artificial Neural Network—0
Customer Support Ticket Escalation Prediction using Feature Engineering—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective—0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
Data-Driven Investigative Journalism For Connectas Dataset—0
Data-driven Smart Ponzi Scheme Detection—0
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
Dataset-Agnostic Recommender Systems—0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison—0
Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data—0
Decision Tree Based Wrappers for Hearing Loss—0
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory—0
Decoding and interpreting cortical signals with a compact convolutional neural network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified