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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 1641–1650 of 1706 papers

TitleStatusHype
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Detecting Troll Tweets in a Bilingual Corpus—0
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified