.png){kind=icon}
.png){kind=icon}
Return
DESReg: Dynamic Ensemble Selection library for Regression tasks
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2024.127487.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Nowadays, regression is a very demanded predictive task to solve a wide range of problems belonging todifferent research and society areas. Examples of applications include industry, economic, medical and energyfields. Ensemble methodology works by merging the output obtained from a set of base methods (learners),achieving successful results in both classification and regression tasks. Traditional ensembles use the output ofthe whole set of base methods, in a static way, to obtain the result of the ensemble. However, latest studiesshow that dynamic selection of learners or even dynamic aggregation of their outputs produce better results.Methodologies that integrate these techniques are called dynamic ensembles or dynamic ensemble selection. Although the literature and tools to work with dynamic ensembles for classification tasks is abundant, forregression tasks these resources are scarcer. This paper aims to mitigate these shortcomings by presenting alibrary for the design, development and execution of dynamic ensembles for regression problems. Specifically,the Python software packageDESRegis presented. This library allows us to access to the latest dynamicensemble techniques in the field, standing out for its high configurability, its support for extending it withuser-defined functions or its parallel computation capabilities.
Keywords:
Dynamic ensemble selection
Regression
Software package
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W