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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 1501–1525 of 1706 papers

TitleStatusHype
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak SupervisionCode0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification—0
A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts—0
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
ICE: Information Credibility Evaluation on Social Media via Representation Learning—0
A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service - Taking Listener's Brainwaves to Extremes—0
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait RecognitionCode0
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement LearningCode0
A deep learning model for estimating story points—0
Unsupervised, Efficient and Semantic Expertise RetrievalCode1
Star-galaxy Classification Using Deep Convolutional Neural NetworksCode0
Applying Deep Learning to Basketball TrajectoriesCode0
Deep Hashing: A Joint Approach for Image Signature Learning—0
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
SHEF-MIME: Word-level Quality Estimation Using Imitation Learning—0
Improving Sequence to Sequence Learning for Morphological Inflection Generation: The BIU-MIT Systems for the SIGMORPHON 2016 Shared Task for Morphological Reinflection—0
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations—0
A Linear Baseline Classifier for Cross-Lingual Pronoun Prediction—0
Word embeddings and discourse information for Quality Estimation—0
Adapting Event Embedding for Implicit Discourse Relation Recognition—0
Shallow Discourse Parsing Using Convolutional Neural Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified