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Advisor(s)
Abstract(s)
Os valores omissos representam um problema frequente no processo
de análise de dados. Neste artigo foram comparados seis métodos distintos de
imputação, disponíveis no software R e avaliado o seu desempenho em
conjuntos de dados relacionados com a área da educação. Foi estudada uma
amostra de 20408 estudantes para testar os seis algoritmos em quatro
conjuntos de dados gerados por simulação com diferentes percentagens de
valores omissos, considerando 5%, 10%, 15% e 20% nas variáveis de
interesse. Foram explorados métodos de imputação simples (Média, Mediana
e Moda), métodos baseados em aprendizagem automática (kNN e bPCA) e um
método de imputação múltipla (MICE). Foi avaliado o desempenho de cada
método calculando os respetivos erros de imputação através as métricas
RMSE e MAE. Os resultados obtidos mostram que a imputação pela Moda
forneceu quase de forma constante menores valores de erro.
Missing values represent a frequent problem in the data analysis process. In this paper, six different imputation methods, available in software R, were used and compared. Their performance was evaluated in datasets related to the education area, namely data from the national evaluation of school performance (Prova Brasil). A sample of 20408 students was studied to test the six algorithms in four subsets of data with different percentages of missing values, considering 5%, 10%, 15% and 20% in the variables of interest. Single imputation methods (Mean, Median and Mode), methods based on machine learning (kNN and bPCA) and a multiple imputation method (MICE) were explored. The performance of each method adopted in this work was evaluated by calculating the respective imputation errors using the metrics RMSE and MAE. The results obtained show that the method of imputation by Mode provided almost constantly lower values of error.
Missing values represent a frequent problem in the data analysis process. In this paper, six different imputation methods, available in software R, were used and compared. Their performance was evaluated in datasets related to the education area, namely data from the national evaluation of school performance (Prova Brasil). A sample of 20408 students was studied to test the six algorithms in four subsets of data with different percentages of missing values, considering 5%, 10%, 15% and 20% in the variables of interest. Single imputation methods (Mean, Median and Mode), methods based on machine learning (kNN and bPCA) and a multiple imputation method (MICE) were explored. The performance of each method adopted in this work was evaluated by calculating the respective imputation errors using the metrics RMSE and MAE. The results obtained show that the method of imputation by Mode provided almost constantly lower values of error.
Description
Keywords
Valores omissos Análise de dados
Citation
Publisher
Centro de Ciência e Tecnologia (CCT) da Universidade Federal de Roraima (UFRR)