Data preparation for data mining
Data preparation for data mining
On the influence of imputation in classification: practical issues
Journal of Experimental & Theoretical Artificial Intelligence
Evolving clusters in gene-expression data
Information Sciences: an International Journal
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This paper proposes a method for substituting missing values that is based on an evolutionary algorithm for clustering. Missing values substitution has been traditionally assessed by some measures of the prediction capability of imputation methods. Although this evaluation is useful, it does not allow inferring the influence of imputed values in the ultimate modeling task (e.g., in classification). In this sense, alternative approaches to the so called prediction capability evaluation are needed. Therefore, we here also discuss the influence of imputed values in the classification task. Preliminary results obtained in a bioinformatics data set illustrate that the proposed imputation algorithm can insert less classification bias than three state of the art algorithms (i.e., KNNimpute, SKNN and IKNN). Finally, we illustrate that better prediction results do not necessarily imply in less classification bias.