Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

Abstract

Based on the datasets from UCI and Obstructive Sleep Apnea, a disparate methodology of uncovering the visualization effects into the pushed support constraints of schema enumerated tree-based classification techniques is proposed and presented in this paper. This is to actively ‘wipe out’ the redundant growing effects of decision trees through itemset generation when visualization techniques are applied using Principal Component Analysis (PCA) and/or Principal Component Variable Grouping (PCVG) algorithms. Enumeration specification is based on the schema enumerated tree (SET) drawn after sorting out the features and characteristics on each dataset applied. The linchpin is to streamline the pre-tree classification effects for post-tree classification by using visualization techniques, i.e. PCA and/or PCVG, which are applied during the SET development. The over-fitting effects done during the SET development by the pushed support constraints can be counter-corrected by fewer PCA and/or PCVG imposed during visualization processes. The under-fitting effects done by the imprecise ‘early stopping’ of the SET development can be counter-corrected by greater PCA and/or PCVG imposed during the post-tree classification techniques through pushed SET support constraint learning. Research outcome on all the investigated datasets showed that the prediction accuracies have been profoundly improved after applying visualization of PCA and/or PCVG algorithms into the pushed SET-based or SET-based support constraints.