Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

ABSTRACT: A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the

SUBMITTER: Mir AA 

PROVIDER: S-EPMC8758196 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature