Version [1.0.3] — [CACP: Classification Algorithms Comparison Pipeline]

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-11-04 DOI:10.1016/j.softx.2024.101938

Sylwester Czmil, Jacek Kluska, Anna Czmil

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引用次数: 0

Abstract

We present the first major release of the Classification Algorithms Comparison Pipeline (CACP). The proposed software enables one to compare newly developed classification algorithms in Python with other classifiers to evaluate classification performance and ensure both outcomes’ reproducibility and statistical reliability. CACP simplifies and accelerates the entire classifier evaluation process considerably and helps prepare the professional documentation of the experiments conducted. The upgrade introduces enhancements to existing tools and adds new features: (1) - support for River machine learning library datasets in incremental learning, (2) - capability to include user-defined datasets, (3) - use of River classifiers for incremental learning, (4) - use of River metrics for incremental learning, (5) - flexibility to create user-defined metrics, (6) - record-by-record testing for incremental learning, (7) - enhanced summary of incremental testing results with dynamic visualization of the learning process, (8) - Graphical User Interface (GUI).

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版本 [1.0.3] - [CACP：分类算法比较管道］

我们推出了分类算法比较管道（CACP）的第一个重要版本。通过该软件，人们可以将新开发的 Python 分类算法与其他分类器进行比较，以评估分类性能，确保结果的可重复性和统计可靠性。CACP 可大大简化和加快整个分类器评估过程，并有助于准备所进行实验的专业文档。此次升级对现有工具进行了改进，并增加了新功能：(1) - 在增量学习中支持 River 机器学习库数据集，(2) - 能够包含用户定义的数据集，(3) - 在增量学习中使用 River 分类器，(4) - 在增量学习中使用 River 指标，(5) - 灵活创建用户定义的指标，(6) - 在增量学习中逐条记录测试，(7) - 增强的增量测试结果汇总，学习过程动态可视化，(8) - 图形用户界面 (GUI)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.