Feature Engineering
392 papers with code • 1 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Feature Engineering
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Libraries
Use these libraries to find Feature Engineering models and implementations
EMBER
Latest papers
Universal Time-Series Representation Learning: A Survey
Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies.
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
NVIDIA/DeepLearningExamples
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While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation.
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets with limited samples.
Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural Networks
We propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms.
Understanding learning from EEG data: Combining machine learning and feature engineering based on hidden Markov models and mixed models
Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.
Auto deep learning for bioacoustic signals
giuliotosato/autokeras-bioacustic
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This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against traditional manually-designed deep learning models.
Classification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health Monitoring
We believe that we are the first to report numerical models for four types of damages in HCSS, which is followed up with experimental validation.
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series
IoannisNasios/M5_Uncertainty_3rd_place
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International Journal of Forecasting 2022
The keypoints of our methodology are: a) transform the task to regression on sales for a single day b) information rich feature engineering c) create a diverse set of state-of-the-art machine learning models and d) carefully construct validation sets for model tuning.
FASER: Binary Code Similarity Search through the use of Intermediate Representations
br0kej/FASER
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Being able to identify functions of interest in cross-architecture software is useful whether you are analysing for malware, securing the software supply chain or conducting vulnerability research.
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
