Xputer:利用 NMF、XGBoost 和简化的图形用户界面体验缩小数据差距。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2024-04-24 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1345179
Saleena Younus, Lars Rönnstrand, Julhash U Kazi
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引用次数: 0

摘要

数据在各个领域的迅速扩散,凸显了准确估算缺失值的重要性。这项任务对于确保数据完整性和获得有意义的见解至关重要。为了应对这一挑战,我们推出了新型估算工具 Xputer,它将非负矩阵因式分解(NMF)与 XGBoost 的预测优势巧妙地结合在一起。Xputer 的突出特点之一是它的多功能性:它支持零归因,通过 Optuna 实现超参数优化,并允许用户定义迭代次数。为了增强用户体验和易用性,我们为 Xputer 配备了直观的图形用户界面(GUI),即使对计算工具不太熟悉的用户也能轻松操作。在性能基准测试中,Xputer 的估算准确性往往优于 IterativeImputer。此外,Xputer 还能自主处理各种类型的数据,包括分类数据、连续数据和布尔数据,无需事先进行预处理。Xputer 集性能、灵活性和用户友好设计于一身,是数据估算领域最先进的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience.

The rapid proliferation of data across diverse fields has accentuated the importance of accurate imputation for missing values. This task is crucial for ensuring data integrity and deriving meaningful insights. In response to this challenge, we present Xputer, a novel imputation tool that adeptly integrates Non-negative Matrix Factorization (NMF) with the predictive strengths of XGBoost. One of Xputer's standout features is its versatility: it supports zero imputation, enables hyperparameter optimization through Optuna, and allows users to define the number of iterations. For enhanced user experience and accessibility, we have equipped Xputer with an intuitive Graphical User Interface (GUI) ensuring ease of handling, even for those less familiar with computational tools. In performance benchmarks, Xputer often outperforms IterativeImputer in terms of imputation accuracy. Furthermore, Xputer autonomously handles a diverse spectrum of data types, including categorical, continuous, and Boolean, eliminating the need for prior preprocessing. Given its blend of performance, flexibility, and user-friendly design, Xputer emerges as a state-of-the-art solution in the realm of data imputation.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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