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 476–500 of 1706 papers

TitleStatusHype
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory—0
Decoding and interpreting cortical signals with a compact convolutional neural network—0
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective—0
A Survey on Extraction of Causal Relations from Natural Language Text—0
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
Deep Attentive Sentence Ordering Network—0
A Survey on Semantics in Automated Data Science—0
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features—0
Deepr: A Convolutional Net for Medical Records—0
A neural network model for solvency calculations in life insurance—0
DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection—0
Deep Exhaustive Model for Nested Named Entity Recognition—0
Deep Feature Learning for Wireless Spectrum Data—0
AutoML-GPT: Large Language Model for AutoML—0
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
Deep Hashing: A Joint Approach for Image Signature Learning—0
Deep Health Care Text Classification—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction—0
A Cognition Based Attention Model for Sentiment Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified