A Comparative and Systematic Study of Machine Learning (ML) Approaches for Particulate Matter (PM) Prediction
Air quality in metropolitan areas has deteriorated due to growing urbanisation and
industrialisation, leading to severe health and significant economic consequences. This …
industrialisation, leading to severe health and significant economic consequences. This …
Handling complex missing data using random forest approach for an air quality monitoring dataset: a case study of Kuwait environmental data (2012 to 2018)
In environmental research, missing data are often a challenge for statistical modeling. This
paper addressed some advanced techniques to deal with missing values in a data set …
paper addressed some advanced techniques to deal with missing values in a data set …
缺失数据处理方法研究综述.
熊中敏， 郭怀宇， 吴月欣 - Journal of Computer Engineering …, 2021 - search.ebscohost.com
大数据时代, 数据爆炸式的增长, 数据获取变得更容易的同时数据缺失现象也更加普遍.
数据的缺失极大地降低了数据的实用性. 数据缺失问题的处理成为大数据处理的热点研究课题 …
数据的缺失极大地降低了数据的实用性. 数据缺失问题的处理成为大数据处理的热点研究课题 …
Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data
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 …
to any machine learning method to efficiently fill in any missing or irregularly sampled data. It …
Comparison of imputation methods for missing values in air pollution data: Case study on Sydney air quality index
Missing values in air quality data may lead to a substantial amount of bias and inefficiency in
modeling. In this paper, we discuss six methods for dealing with missing values in univariate …
modeling. In this paper, we discuss six methods for dealing with missing values in univariate …
Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM
Background Missing observations within the univariate time series are common in real-life
and cause analytical problems in the flow of the analysis. Imputation of missing values is an …
and cause analytical problems in the flow of the analysis. Imputation of missing values is an …
Handling missing data in a rheumatoid arthritis registry using random forest approach
Missing data in clinical epidemiological research violate the intention‐to‐treat principle,
reduce the power of statistical analysis, and can introduce bias if the cause of missing data …
reduce the power of statistical analysis, and can introduce bias if the cause of missing data …
A complete air pollution monitoring and prediction framework
The issue of air pollution is increasingly prominent and represents a significant
environmental challenge, particularly in urban areas affected by rising migration rates. Air …
environmental challenge, particularly in urban areas affected by rising migration rates. Air …
Time Series Reconstruction With Feature-Driven Imputation: A Comparison of Base Learning Algorithms
Addressing the challenge of missing values is a critical step when preparing and analyzing
data. This process, known as imputation, helps ensure the dataset is complete, accurate …
data. This process, known as imputation, helps ensure the dataset is complete, accurate …
An empirical comparison of the sales forecasting performance for plastic tray manufacturing using missing data
The problem of missing data is frequently met in time series analysis. If not appropriately
addressed, it usually leads to failed modeling and distorted forecasting. To deal with high …
addressed, it usually leads to failed modeling and distorted forecasting. To deal with high …