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About 10,000 papers for “feature engineering”

Feature Engineering

Matthias Schonlau · Statisctics and computing/Statistics and computing · 2023

Cited by 0

Feature Engineering

Karthik Ramasubramanian, Abhishek Singh · Apress eBooks · 2016

In machine learning, feature engineering is a blanket term covering both statistical and business judgment aspects of modeling real-world problems. Feature engineering is a new term coined recently to give due importance to the domain knowledge required to select sets of features for machine learning algorithms. It is one of the reasons that most of the machine learning professionals call it an informal process. In this chapter, we will provide an easy-to-use guide of key terms and methodology used in feature engineering. The chapter will give due weight to the domain knowledge and some common business limitations while using machine learning algorithms to solve business problems.

Cited by 5

Feature engineering

Turner C. R., Fuggetta A., Wolf A. L. et al. · IrInSubria (University of Insubria) · 1998

The notion of feature is widely used to denote the functional structure and visible properties of a software system. More specifically, features are meant to represent a user-centric organization of a software system's functionality. Yet, other than during requirements analysis, features are seldom treated explicitly by most existing tools and methods.This paper argues that a feature orientation can provide benefits to software developers throughout the software life cycle. We envisage specific applications of the notion of feature that provide a powerful and unifying structure for software life cycle artifacts and activities. We discuss the problems and issues to be addressed, a brief summary of our current research work, and suggestions and directions for future research in a new area we call "feature engineering".

Cited by 0

Feature engineering.

Youli Jiang (15249070), Ao Li (15375), Zhihuan Li (10233018) et al. · Figshare · 2025

(A) Original sample visualization using t-SNE shows an imbalanced outcome. (B) Random undersampling addresses imbalanced data by decreasing the majority class. (C) Random oversampling increases the minority class. (D) Synthetic Minority Over-sampling Technique with Nominal Continuous (SMOTE-NC) synthesizes data from the minority class. (E) Lasso Regression Coefficients: Indicates the influence of each feature as determined by the Lasso model. (F) Lasso coefficient path diagram.

Cited by 0Open access

Feature Engineering

Sorin Soviany, Cristina Soviany · Transactions on computational science and computational intelligence · 2020

Cited by 2

Feature Engineering

Yuan Yao, Xing Su, Hanghang Tong · SpringerBriefs in computer science · 2018

Cited by 2

Feature engineering.

Nikola Milojevic-Dupont (9749755), Nicolai Hans (9749758), Lynn H. Kaack (9749761) et al. · Figshare · 2020

<p>In this appendix, we explain in detail how each feature group has been implemented.</p> <p>(PDF)</p>

Cited by 0Open access

Feature Engineering

Daniel Christiansen, Jerome Claracq, S. Mukhopadhyay · 2024

The data-driven approach to ML prediction of material properties from compositional data (i.e., quantities of component ingredients – referred to as “formulation ratios” in Chapter 4) has several benefits, such as rapid and inexpensive development time and source-agnosticism. However, model performance, usability, and trustworthiness can suffer when the physical mechanisms of the system are not represented in the compositional data alone. Moreover, using compositional data inflates the number of variables, as all ingredients must be encoded even when not used in a formulation. Another drawback is that having multiple variables with many zeros may not suit all modeling techniques and will require a larger training data set. This chapter describes methods to supplement or replace such data with descriptors measured, calculated, or retrieved from more analytical and empirical sources. The primary focus of these techniques is to incorporate principles of physics and chemistry to enrich the data while simultaneously reducing the number and complexity of composition-based descriptors.

Cited by 0

Feature engineering

Carlos A. Escobar, Rubén Morales-Menéndez · Elsevier eBooks · 2024

Cited by 0

Feature Selection and Feature Engineering

Hisham El-Amir, Mahmoud Hamdy · Apress eBooks · 2019

Feature selection and engineering are important steps in a machine learning pipeline and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.

Cited by 4

R Feature Engineering

Pearce, Justine · Figshare · 2024

We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25-49 days old) were used to train and test the algorithm and, unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains the random forest model classified behaviours with very good accuracy (92%), specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%).

Cited by 0Open access

Multiobjective Feature Engineering

Batista, João Eduardo, Rodrigues, Nuno Miguel, Vanneschi, Leonardo et al. · Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa) · 2024

Batista, J. E., Rodrigues, N. M., Vanneschi, L., & Silva, S. (2024). M6GP: Multiobjective Feature Engineering. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10612107 --- This work was supported by FCT through the LASIGE (UIDB/00408/20203 and UIDP/00408/20204) and MagIC/NOVA IMS (UIDB/04l52/2020) research units, and PhD Grant 202l/05322/BD

Cited by 0

Feature Engineering Infrastructure

Hien Luu, Max Pumperla, Zhe Zhang · Apress eBooks · 2024

This chapter and the following infrastructure-related chapters are designed to dive into details about the infrastructure of the MLOps stack, namely, feature engineering infrastructure, model training infrastructure, and model serving infrastructure. For these chapters, the following structure will be used. First, the high-level details and benefits will be described. Next, the high-level architecture and its subcomponents will be discussed, and finally a few case studies, including home-grown, open source, and commercial vendor solutions, will be highlighted.

Cited by 0

R Feature Engineering

Pearce, Justine · Figshare · 2024

We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25-49 days old) were used to train and test the algorithm and, unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains the random forest model classified behaviours with very good accuracy (92%), specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%).

Cited by 0Open access