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 1426–1450 of 1706 papers

TitleStatusHype
Interpretation of Semantic Tweet RepresentationsCode0
Syntax Aware LSTM Model for Chinese Semantic Role Labeling—0
Sentiment Analysis of Citations Using Word2vecCode0
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models—0
The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger—0
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification—0
A Multi-task Approach to Predict Likability of Books—0
Neural Networks for Negation Cue Detection in Chinese—0
Automatic Argumentative-Zoning Using Word2vecCode0
Supervised Typing of Big Graphs using Semantic Embeddings—0
One-Shot Imitation Learning—0
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methodsCode0
Machine learning approach for early detection of autism by combining questionnaire and home video screening—0
Building automated vandalism detection tools for Wikidata—0
Deep Voice: Real-time Neural Text-to-SpeechCode0
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting—0
Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing—0
Multitask Learning with Deep Neural Networks for Community Question Answering—0
A Morphology-aware Network for Morphological DisambiguationCode0
Graph Neural Networks and Boolean Satisfiability—0
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
A Deep Convolutional Neural Network for Background Subtraction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified