Treating Missing Values in INAR(1) Models

Inductive learning models with missing values

In this paper, a new approach to working with missing attribute values in inductive learning algorithms is introduced. Three fundamental issues are studied: the splitting criterion, the allocation of values to missing attribute values, and the prediction of new observations. The formal definition for the splitting criterion is given. This definition takes into account the missing attribute valu...

Handling Missing Values when Applying Classication Models

Much work has studied the e¤ect of di¤erent treatments of missing values on model induction, but little work has analyzed treatments for the common case of missing values at prediction time. This paper …rst compares several di¤erent methods— predictive value imputation, the distribution-based imputation used by C4.5, and using reduced models— for applying classi…cation trees to instances with m...

Handling Missing Values when Applying Classification Models

Much work has studied the effect of different treatments of missing values on model induction, but little work has analyzed treatments for the common case of missing values at prediction time. This paper first compares several different methods—predictive value imputation, the distributionbased imputation used by C4.5, and using reduced models—for applying classification trees to instances with...

Maximum Likelihood Estimation in Graphical Models with Missing Values

Dealing with missing predictor values when applying clinical prediction models.

BACKGROUND Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with such missing values. We evaluated 6 strategies to deal with missing values. METHODS We developed an...