Feature Engineering
393 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.
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Libraries
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Latest papers with no code
Automated data processing and feature engineering for deep learning and big data applications: a survey
In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces
To improve the accuracy of a small model, knowledge distillation is a popular method.
Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning
This demonstrates the potential of stacking to improve probabilistic predictions in spatial interpolation and beyond.
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound.
Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures
This paper introduces a robust approach for automated defect detection in tire X-ray images by harnessing traditional feature extraction methods such as Local Binary Pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) features, as well as Fourier and Wavelet-based features, complemented by advanced machine learning techniques.
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model
Large language models(LLM) such as ChatGPT have substantially simplified the generation of marketing copy, yet producing content satisfying domain specific requirements, such as effectively engaging customers, remains a significant challenge.
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models
This paper proposes a novel conceptual prototype for designing versatile graph learning methods with LLMs, with a particular focus on the "where" and "how" perspectives.
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments.
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface
Whilst a legitimate RIS can yield beneficial impacts including increased channel randomness to enhance physical layer secret key generation (PL-SKG), malicious RIS can poison legitimate channels and crack most of existing PL-SKGs.
Unraveling the Key of Machine Learning Solutions for Android Malware Detection
Android malware detection serves as the front line against malicious apps.
