Xputer : bridging data gaps with NMF, XGBoost, and a streamlined GUI experience
Younus, Saleena LU ; Rönnstrand, Lars LU
and Kazi, Julhash U.
LU
(2024)
In Frontiers in Artificial Intelligence
7.
p.01-11
- Abstract
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... (More)
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.
(Less)
- author
- Younus, Saleena
LU
; Rönnstrand, Lars
LU
and Kazi, Julhash U.
LU
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ensemble learning, imputation, matrix factorization, mix-type data, tabular data
- in
- Frontiers in Artificial Intelligence
- volume
- 7
- article number
- 1345179
- pages
- 01 - 11
- publisher
- Frontiers Media S. A.
- external identifiers
-
- pmid:38720912
- scopus:85192392140
- ISSN
- 2624-8212
- DOI
- 10.3389/frai.2024.1345179
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: Copyright © 2024 Younus, Rönnstrand and Kazi.
- id
- 912b3d1f-4f34-4e2d-b23a-2d51fdb86602
- date added to LUP
- 2025-01-14 15:57:00
- date last changed
- 2025-07-02 19:12:06
@article{912b3d1f-4f34-4e2d-b23a-2d51fdb86602,
abstract = {{<p>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.</p>}},
author = {{Younus, Saleena and Rönnstrand, Lars and Kazi, Julhash U.}},
issn = {{2624-8212}},
keywords = {{ensemble learning; imputation; matrix factorization; mix-type data; tabular data}},
language = {{eng}},
pages = {{01--11}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Artificial Intelligence}},
title = {{Xputer : bridging data gaps with NMF, XGBoost, and a streamlined GUI experience}},
url = {{http://dx.doi.org/10.3389/frai.2024.1345179}},
doi = {{10.3389/frai.2024.1345179}},
volume = {{7}},
year = {{2024}},
}