Machine Learning-Based Imputation Techniques Analysis and Study

Title

Machine Learning-Based Imputation Techniques Analysis and Study

Creator

Pindiyan A.V.; Pramila R.M.

Description

Missing values are a significant problem in data analysis and machine learning applications. This study looks at the efficacy of machine learning (ML) - based imputation strategies for dealing with missing data. K-nearest Neighbours (KNN), Random Forest, Support Vector Machines (SVM), and Median/Mean Imputation were among the techniques explored. To address the issue of missing data, the study employs k-nearest neighbors, Random Forests, and SVM algorithms. The dataset's imbalance is considered, and the mean F1 score is employed as an evaluation criterion, using cross-validation to ensure consistent results. The study aims to identify the most effective imputation strategy within ML models, offering crucial insights about their adaptability across various scenarios. The study aims to determine the best plan for data preprocessing in machine learning by comparing approaches. Finally, the findings help to improve our knowledge and application of imputation techniques in real-world data analysis and machine learning. 2024 IEEE.

Source

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

imputation technique; KNN; Missing Data; Random Forest; SVM

Coverage

Pindiyan A.V., CHRIST University, Department of Data Science, Bangalore, India; Pramila R.M., CHRIST University, Department of Data Science, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 979-835037809-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICEECT61758.2024.10739330

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211147858&doi=10.1109%2fICEECT61758.2024.10739330&partnerID=40&md5=99c1155a500a68eb3ad90091c3eee417