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 876–900 of 1706 papers

TitleStatusHype
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method—0
Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study—0
Differentiable Sparsification for Deep Neural Networks—0
Differentiable Sparsification for Deep Neural Networks—0
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distinguishing Risk Preferences using Repeated Gambles—0
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
DNN2LR: Automatic Feature Crossing for Credit Scoring—0
Do LSTMs really work so well for PoS tagging? -- A replication study—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling—0
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation—0
DROCC: Deep Robust One-Class Classification—0
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—0
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers—0
Dual Training and Dual Prediction for Polarity Classification—0
DUBLIN -- Document Understanding By Language-Image Network—0
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles—0
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations—0
Dynamic Adaptation in Data Storage: Real-Time Machine Learning for Enhanced Prefetching—0
Dynamic and Adaptive Feature Generation with LLM—0
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network—0
Show:102550
← PrevPage 36 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified