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 1126–1150 of 1706 papers

TitleStatusHype
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM—0
An Analysis of Encoder Representations in Transformer-Based Machine Translation—0
Tackling Sequence to Sequence Mapping Problems with Neural Networks—0
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition—0
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization—0
Exploring Adversarial Examples in Malware Detection—0
INFODENS: An Open-source Framework for Learning Text RepresentationsCode0
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities—0
Spikebench: An open benchmark for spike train time-series classificationCode0
Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Health—0
A Comparative Study of Neural Network Models for Sentence Classification—0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Syntax Encoding with Application in Authorship Attribution—0
SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering—0
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction—0
Deep Exhaustive Model for Nested Named Entity Recognition—0
Deep Attentive Sentence Ordering Network—0
Treatment Side Effect Prediction from Online User-generated Content—0
Cross-lingual Knowledge Graph Alignment via Graph Convolutional NetworksCode0
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment AnalysisCode0
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition—0
Revisiting neural relation classification in clinical notes with external informationCode0
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks—0
IIT(BHU)--IIITH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological ReinflectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified