Separate-and-conquer survival action rule learning

Abstract

Action mining is a data mining method that aims to identify recommendations for changing attribute values that can lead to the classification of data instances as examples of another class. Action mining algorithms extract rules containing recommendations in the premises and class changes in the conclusion. To the best of the authors’ knowledge, no method has been proposed yet for generating action rules based on censored data. This study introduces the first method for survival action rule generation. The method stems from the covering rule induction algorithm but generates rules defining the actions required to change not the class but the survival curve of the covered examples. Thus, this study poses a new research problem: generating action rules for censored data and survival analysis. This study evaluated the proposed method using 22 data sets in which two application domains of survival analysis were distinguished: medicine and predictive maintenance. In addition, more detailed analyses of the generated survival action rules were presented in the form of case studies for the two selected data sets. The results show that the proposed method generates good-quality survival action rules and changes in the survival curves, resulting from the identified actions are significant.