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 1301–1350 of 1706 papers

TitleStatusHype
Neurology-as-a-Service for the Developing World—0
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text ScoringCode0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning—0
Deep Learning in Lexical Analysis and Parsing—0
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure—0
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory—0
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
Deep Health Care Text Classification—0
Solving the "false positives" problem in fraud predictionCode0
Clickbait Detection in Tweets Using Self-attentive NetworkCode0
End-to-end Network for Twitter Geolocation Prediction and HashingCode0
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip—0
Identifying Quantum Phase Transitions with Adversarial Neural NetworksCode0
Clickbait detection using word embeddings—0
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks—0
A Deep Neural Network Approach To Parallel Sentence Extraction—0
Graph Convolutional Networks for Named Entity RecognitionCode0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges—0
Investigating how well contextual features are captured by bi-directional recurrent neural network models—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages—0
Feature-Enriched Character-Level Convolutions for Text Regression—0
Transparent text quality assessment with convolutional neural networks—0
A study of N-gram and Embedding Representations for Native Language IdentificationCode0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Syntax Aware LSTM model for Semantic Role Labeling—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Deep Neural Solver for Math Word Problems—0
A Cognition Based Attention Model for Sentiment Analysis—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Do LSTMs really work so well for PoS tagging? -- A replication study—0
Recurrent Attention Network on Memory for Aspect Sentiment AnalysisCode0
A Factored Neural Network Model for Characterizing Online Discussions in Vector SpaceCode0
Getting the Most out of AMR Parsing—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
Sales Forecast in E-commerce using Convolutional Neural Network—0
Deep Style Match for Complementary Recommendation—0
Automated Website Fingerprinting through Deep LearningCode1
Energy-based Models for Video Anomaly Detection—0
Deep & Cross Network for Ad Click PredictionsCode1
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method—0
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition—0
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified