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 1351–1400 of 1706 papers

TitleStatusHype
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks—0
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods—0
Clinical Event Detection with Hybrid Neural Architecture—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Extracting Drug-Drug Interactions with Attention CNNs—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text—0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task—0
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain—0
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity—0
ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
Semantic Frame Labeling with Target-based Neural Model—0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison—0
PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge—0
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering—0
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification—0
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings—0
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—0
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles—0
A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings SurprisesCode0
Hyperbolic Representation Learning for Fast and Efficient Neural Question AnsweringCode0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Learning to Rank Question Answer Pairs with Holographic Dual LSTM ArchitectureCode0
Generalized Convolutional Neural Networks for Point Cloud Data—0
Automation of Feature Engineering for IoT Analytics—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network—0
Varying Linguistic Purposes of Emoji in (Twitter) Context—0
Predicting Depression for Japanese Blog Text—0
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering—0
A Local Detection Approach for Named Entity Recognition and Mention Detection—0
Deep Learning in Semantic Kernel Spaces—0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
Interpretable Predictions of Tree-based Ensembles via Actionable Feature TweakingCode0
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR ModelsCode0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
Recognizing irregular entities in biomedical text via deep neural networks—0
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem—0
One button machine for automating feature engineering in relational databases—0
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget—0
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning—0
Robust Tracking Using Region Proposal Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified