Explicit Contrast Patterns Versus Minimal Jumping Emerging Patterns for Lazy Classification in High Dimensional Data
Marzena Kryszkiewicz , Przemysław Podsiadły
AbstractMinimal jumping emerging patterns have been proved very useful for classification purposes. Nevertheless, the determination of minimal jumping emerging patterns may require evaluation of candidate patterns, the number of which might be exponential with respect to the dimensionality of a data set. This property may disallow classification by means of minimal jumping emerging patterns in the case of high dimensional data. In this paper, we derive an upper bound on the lengths of minimal jumping emerging patterns and an upper bound on their number. We also propose an alternative approach to lazy classification which uses explicit contrast patterns instead of minimal jumping emerging patterns, but produces the same classification quality as a lazy classifier based on minimal jumping emerging patterns. We argue that our approach, unlike the approach based on minimal jumping emerging patterns, can be applied in the case of high dimensional data.
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