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This paper presents an approach for transforming data granularity inhierarchical databases for binary decision problems by applying regression tocategorical attributes at the lower grain levels. Attributes from a lowerhierarchy entity in the relational database have their information contentoptimized through regression on the categories histogram trained on a smallexclusive labelled sample, instead of the usual mode category of thedistribution. The paper validates the approach on a binary decision task forassessing the quality of secondary schools focusing on how logistic regressiontransforms the students and teachers attributes into school attributes.Experiments were carried out on Brazilian schools public datasets via 10-foldcross-validation comparison of the ranking score produced also by logisticregression. The proposed approach achieved higher performance than the usualdistribution mode transformation and equal to the expert weighing approachmeasured by the maximum Kolmogorov-Smirnov distance and the area under the ROCcurve at 0.01 significance level.

Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining