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
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Libraries
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EMBER
Latest papers with no code
Large Language Models for Networking: Workflow, Advances and Challenges
The networking field is characterized by its high complexity and rapid iteration, requiring extensive expertise to accomplish network tasks, ranging from network design, diagnosis, configuration and security.
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches
This study employs deep learning techniques to explore four speaker profiling tasks on the TIMIT dataset, namely gender classification, accent classification, age estimation, and speaker identification, highlighting the potential and challenges of multi-task learning versus single-task models.
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification
Light curves serve as a valuable source of information on stellar formation and evolution.
Survey on Embedding Models for Knowledge Graph and its Applications
Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities.
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test.
Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts
After the launch of ChatGPT v. 4 there has been a global vivid discussion on the ability of this artificial intelligence powered platform and some other similar ones for the automatic production of all kinds of texts, including scientific and technical texts.
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0
We conclude that the BERT base model will be improved by incorporating the features.
AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
Parkinson's Disease (PD) is the second most common neurodegenerative disorder.
Leveraging Machine Learning for Early Autism Detection via INDT-ASD Indian Database
Using the proposed model, we succeeded in predicting ASD using a minimized set of 20 questions rather than the 28 questions presented in AMI with promising accuracy.
