Case study of inaccuracies in the granulation of decision trees

Abstract

Cybernetics studies information process in the context of interaction with physical systems. Because such information is sometimes vague and exhibits complex interactions; it can only be discerned using approximate representations. Machine learning provides solutions that create approximate models of information and decision trees are one of its main components. However, decision trees are susceptible to information overload and can get overly complex when a large amount of data is inputted in them. Granulation of decision tree remedies this problem by providing the essential structure of the decision tree, which can decrease its utility. To evaluate the relationship that exists between granulation and decision tree complexity, data uncertainty and prediction accuracy, the deficiencies obtained by nursing homes during annual inspections were taken as a case study. Using rough sets, three forms of granulation were performed: (1) attribute grouping, (2) removing insignificant attributes and (3) removing uncertain records. Attribute grouping significantly reduces tree complexity without having any strong effect upon data consistency and accuracy. On the other hand, removing insignificant features decrease data consistency and tree complexity, while increasing the error in prediction. Finally, decrease in the uncertainty of the dataset results in an increase in accuracy and has no impact on tree complexity.