Year: 2025
Type: article
Abstract: Incomplete data poses a significant obstacle in data science and machine learning, influencing model outcomes and occurring commonly across domains such as health, nutrition, electricity, agriculture, chemistry and water resources. Missing data refers to the absence of information for one or more variables in a dataset. Accurate imputation is therefore crucial to ensure the reliability and validity of analyses and predictive models. This study proposes a Harmony Search Algorithm (HSA) to address missing-data imputation, emphasising its flexibility and adaptability. The approach seeks the best imputations by minimising error metrics such as MAE, MSE and RMSE. Computational tests indicate that HSA is a promising method for imputing missing data in a range of contexts.
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415089379
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