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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1375 of 1706 papers

TitleStatusHype
Sales Forecast in E-commerce using Convolutional Neural Network—0
Deep Style Match for Complementary Recommendation—0
Energy-based Models for Video Anomaly Detection—0
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method—0
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks—0
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition—0
Neural Vector Spaces for Unsupervised Information RetrievalCode0
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification—0
PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge—0
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings—0
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing—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
Extracting Drug-Drug Interactions with Attention CNNs—0
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering—0
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain—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
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison—0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
Clinical Event Detection with Hybrid Neural Architecture—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified