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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 851–875 of 1706 papers

TitleStatusHype
DeepLink: A Novel Link Prediction Framework based on Deep Learning—0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction—0
Deep Neural Baselines for Computational Paralinguistics—0
Deep Neural Mobile Networking—0
Deep Neural Solver for Math Word Problems—0
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets—0
Deepr: A Convolutional Net for Medical Records—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Deep Recurrent Neural Network for Protein Function Prediction from Sequence—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
DeepSoft: A vision for a deep model of software—0
Deep Style Match for Complementary Recommendation—0
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature—0
Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified