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 1201–1250 of 1706 papers

TitleStatusHype
Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimizationCode0
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach—0
EmotionX-SmartDubai\_NLP: Detecting User Emotions In Social Media Text—0
Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
Character-level Supervision for Low-resource POS Tagging—0
Self-regulation: Employing a Generative Adversarial Network to Improve Event DetectionCode0
Extracting Relational Facts by an End-to-End Neural Model with Copy MechanismCode0
Named Entity Recognition With Parallel Recurrent Neural NetworksCode0
Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksCode0
Syntax for Semantic Role Labeling, To Be, Or Not To BeCode0
Stock Movement Prediction from Tweets and Historical PricesCode0
Product-based Neural Networks for User Response Prediction over Multi-field Categorical DataCode0
Semi-supervised Seizure Prediction with Generative Adversarial Networks—0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
Binary Classification in Unstructured Space With Hypergraph Case-Based ReasoningCode0
ServeNet: A Deep Neural Network for Web Services ClassificationCode0
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine TranslationCode0
Explainable Neural Networks based on Additive Index Models—0
Estimating Linguistic Complexity for Science TextsCode0
Complex Word Identification: Convolutional Neural Network vs. Feature Engineering—0
Feature Engineering for Second Language Acquisition Modeling—0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification—0
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health—0
HCCL at SemEval-2018 Task 8: An End-to-End System for Sequence Labeling from Cybersecurity Reports—0
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets—0
CTSys at SemEval-2018 Task 3: Irony in Tweets—0
Deep Affix Features Improve Neural Named Entity RecognizersCode0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods—0
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM—0
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection—0
LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification—0
SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification—0
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
THU\_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task LearningCode0
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees—0
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
Multi-Scale DenseNet-Based Electricity Theft Detection—0
A Brand-level Ranking System with the Customized Attention-GRU Model—0
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Extended pipeline for content-based feature engineering in music genre recognition—0
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping—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