Enhanced Missing Data Imputation Using Intuitionistic Fuzzy Rough-Nearest Neighbor Approach

Shivani Singh

2024

Abstract

The exponential growth of databases across various domains necessitates robust techniques for handling missing data to maintain data integrity and analytical accuracy. Traditional approaches often struggle with real-valued datasets due to inherent limitations in handling uncertainty and imprecision. Nearest Neighbourhood algorithms have proven beneficial in missing data imputation, offering effective solutions to address data gaps. In this paper, we propose a novel method for missing data imputation, termed Intuitionistic Fuzzy Rough-Nearest Neighbourhood Imputation (IFR-NNI), which extends the application of intuitionistic fuzzy rough sets to handle missing data scenarios. By integrating Intuitionistic Fuzzy Rough Sets into the nearest neighbor imputation framework, we aim to overcome the limitations of traditional methods, including information loss, challenges in managing uncertainty and vagueness, and instability in approximation outcomes. The proposed method is implemented on real-valued datasets, and non-parametric statistical analysis is performed to evaluate its performance. Our findings indicate that the IFR-NNI method demonstrates excellent performance in general, showcasing its effectiveness in addressing missing data scenarios and advancing the field of data imputation methodologies.

Paper Citation

in Harvard Style

Singh S. (2024). Enhanced Missing Data Imputation Using Intuitionistic Fuzzy Rough-Nearest Neighbor Approach. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: FCTA; ISBN 978-989-758-721-4, SciTePress, pages 399-407. DOI: 10.5220/0013015600003837

in Bibtex Style

@conference{fcta24,
author={Shivani Singh},
title={Enhanced Missing Data Imputation Using Intuitionistic Fuzzy Rough-Nearest Neighbor Approach},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: FCTA},
year={2024},
pages={399-407},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013015600003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: FCTA
TI - Enhanced Missing Data Imputation Using Intuitionistic Fuzzy Rough-Nearest Neighbor Approach
SN - 978-989-758-721-4
AU - Singh S.
PY - 2024
SP - 399
EP - 407
DO - 10.5220/0013015600003837
PB - SciTePress