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
PADME: A Deep Learning-based Framework for Drug-Target Interaction PredictionCode0
Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks—0
Automated Treatment Planning in Radiation Therapy using Generative Adversarial NetworksCode0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
DeepInf: Social Influence Prediction with Deep LearningCode0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Towards Non-Parametric Learning to Rank—0
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks—0
EmotionX-SmartDubai\_NLP: Detecting User Emotions In Social Media Text—0
Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimizationCode0
Character-level Supervision for Low-resource POS Tagging—0
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Extracting Relational Facts by an End-to-End Neural Model with Copy MechanismCode0
Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksCode0
Product-based Neural Networks for User Response Prediction over Multi-field Categorical DataCode0
Syntax for Semantic Role Labeling, To Be, Or Not To BeCode0
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
Stock Movement Prediction from Tweets and Historical PricesCode0
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks—0
Named Entity Recognition With Parallel Recurrent Neural NetworksCode0
Self-regulation: Employing a Generative Adversarial Network to Improve Event DetectionCode0
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach—0
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
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
Complex Word Identification: Convolutional Neural Network vs. Feature Engineering—0
CTSys at SemEval-2018 Task 3: Irony in Tweets—0
Deep Affix Features Improve Neural Named Entity RecognizersCode0
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
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 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models—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
Estimating Linguistic Complexity for Science TextsCode0
Feature Engineering for Second Language Acquisition Modeling—0
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection—0
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health—0
LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification—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
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification—0
Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified