title: Censoring weighted separate-and-conquer rule induction from survival data.
abstract: OBJECTIVE
  Rule induction is one of the major methods of machine learning. Rule-based models can be easily read and interpreted by humans, that makes them particularly useful in survival studies as they can help clinicians to better understand analysed data and make informed decisions about patient treatment. Although of such usefulness, there is still a little research on rule learning in survival analysis. In this paper we take a step towards rule-based analysis of survival data.
  
  
  METHODS
  We investigate so-called covering or separate-and-conquer method of rule induction in combination with a weighting scheme for handling censored observations. We also focus on rule quality measures being one of the key elements differentiating particular implementations of separate-and-conquer rule induction algorithms. We examine 15 rule quality measures guiding rule induction process and reflecting a wide range of different rule learning heuristics.
  
  
  RESULTS
  The algorithm is extensively tested on a collection of 20 real survival datasets and compared with the state-of-the-art survival trees and random survival forests algorithms. Most of the rule quality measures outperform Kaplan-Meier estimate and perform at least equally well as tree-based algorithms.
  
  
  CONCLUSIONS
  Separate-and-conquer rule induction in combination with weighting scheme is an effective technique for building rule-based models of survival data which, according to predictive accuracy, are competitive with tree-based representations.
authors.affiliation: Łukasz Wróbel, Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland, E-mail: lukasz.wrobel@polsl.pl.
authors.forename: Ł
authors.initials: Ł
authors.lastname: Wróbel
authors.forename: M
authors.initials: M
authors.lastname: Sikora
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D066264:Datasets as Topic; D003661:Decision Support Techniques; D001330:Electronic Data Processing; D006801:Humans; D016019:Survival Analysis
keywords: Survival prediction; rule induction; rule quality measures