Towards optimal use of incomplete classification data
Data for classification are often incomplete. The multiple-values construction method (MVCM) can be used to include data with missing values for classification. In this study, the MVCM is implemented by using fuzzy sets theory in the context of classification with discrete data. By using the fuzzy sets based MVCM, data with missing values can add values to classification, but can also introduce excessive uncertainty. Furthermore, the computational cost for the use of incomplete data could be prohibitive if the scale of missing values is large. This paper discusses the association between classification performance and the use of incomplete data. It proposes an algorithm of near-optimal use of incomplete classification data. An experiment with real-world data demonstrates the usefulness of the algorithm.
classification multiple-values construction missing values fuzzy sets incomplete data