Dealing with missing values is a difficult problem. The programming in itself is not a problem; we just report the missing value by a specific code. In contrast, the treatment before or during data analysis is very complicated.
Various techniques are available in order to handle missing values into SIPINA. In this tutorial, we show how to implement them; and what are their consequences on the decision tree learning context (C4.5 algorithm; Quinlan, 1993).
Keywords: missing value, missing data, listwise deletion, casewise deletion, data imputation, C4.5, decision tree
Tutorial: en_Sipina_Missing_Data.pdf
Dataset: ronflement_missing_data.zip
References:
P.D. Allison, « Missing Data », in Quantitative Applications in the Social Sciences Series n°136, Sage University Paper, 2002.
J. Bernier, D. Haziza, K. Nobrega, P. Whitridge, « Handling Missing Data – Case Study », Statistical Society of Canada.
D. Garson, "Data Imputation for Missing Values"
