Three Methods For Covering Missing Input Data In Xcs

Missing data pose a potential threat to learning and classification in that they may compromise the ability of a system to develop robust, generalized models of the environment in which they operate. This investigation reports on the effects of three approaches to covering these data using an XCS-style learning classifier system. Using fabricated datasets representing a wide range of missing value densities, it was found that missing data do not appear to adversely affect LCS learning and classification performance. Furthermore, three types of missing value covering were found to exhibit similar efficiency on these data, with respect to convergence rate and classification accuracy.