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Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing

Adil Aslam Mir1,2, Kimberlee Jane Kearfott3, Fatih Vehbi Çelebi1

  • 1Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ayvalı, Keçiören/Ankara, Turkey.

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A new imputation by feature importance (IBFI) method efficiently handles missing data for machine learning. IBFI outperforms traditional methods, particularly with complex missing data scenarios in environmental monitoring.

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Area of Science:

  • Environmental Science
  • Data Science
  • Machine Learning

Background:

  • Missing data is a common challenge in machine learning, impacting model performance and reliability.
  • Existing imputation methods may struggle with complex missingness patterns, including missing not at random (MNAR).
  • Accurate data imputation is crucial for analyzing environmental datasets like soil radon gas concentration (SRGC).

Purpose of the Study:

  • To introduce and evaluate a novel imputation methodology: Imputation by Feature Importance (IBFI).
  • To assess IBFI's effectiveness across various missing data types (MCAR, MAR, MNAR).
  • To compare IBFI's performance against traditional imputation techniques using SRGC data.

Main Methods:

  • IBFI utilizes feature importance derived from a base learning algorithm (XGBoost) to iteratively impute missing values.
  • The method assumes that the target variable (SRGC) depends on environmental parameters (temperature, humidity).
  • Performance was evaluated using metrics like RMSE, MSLE, MAPE, PB, and MSE on simulated and real-world SRGC time-series data.

Main Results:

  • IBFI demonstrated efficient handling of missing data, outperforming mean, median, mode, PMM, and hot-deck methods.
  • The method showed particular advantage in scenarios with multiple missing variables, often a challenge for other techniques.
  • IBFI proved effective for imputing soil radon gas concentration (SRGC) data, even with complex missingness patterns.

Conclusions:

  • IBFI is a robust and efficient imputation technique applicable to diverse machine learning tasks and missing data scenarios.
  • The method's reliance on feature importance provides a physically meaningful approach to data imputation.
  • IBFI offers a valuable advancement for environmental data analysis and other fields facing data imputation challenges.