Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: This paper presents an approach for transforming data granularity in hierarchical databases for binary decision problems by applying regression to categorical attributes at the lower grain levels. Attributes from a lower hierarchy entity in the relational database have their information content optimized through regression on the categories ́ histogram trained on a small exclusive labelled sample, instead of the usual mode category of the distribution. The paper validates the approach on a binary decision task for assessing the quality of secondary schools focusing on how logistic regression transforms the students ́ and teachers ́ attributes into school attributes. Experiments were carried out on Brazilian schools ́ public datasets via 10-fold crossvalidation comparison of the ranking score produced also by logistic regression. The proposed approach achieved higher performance than the usual distribution mode transformation and equal to the expert weighing approach measured by the maximum Kolmogorov-Smirnov distance and the area under the ROC curve at 0.01 significance level. Keywords—Granularity transformation; Categorical attributes; Educational data mining; Relational databases; Distribution mode; Regression ACM-Classification I.2 ARTIFICIAL INTELLIGENCE H.2.8 Database Applications, J.1 ADMINISTRATIVE DATA PROCESSING-Education;

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