Methods Inf Med - Censoring weighted separate-and-conquer rule induction from survival data.

Tópicos

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Resumo

JECTIVES: 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.

Resumo Limpo

jectiv rule induct one major method machin learn rulebas model can easili read interpret human make particular use surviv studi can help clinician better understand analys data make inform decis patient treatment although use still littl research rule learn surviv analysi paper take step toward rulebas analysi surviv datamethod investig socal cover separateandconqu method rule induct combin weight scheme handl censor observ also focus rule qualiti measur one key element differenti particular implement separateandconqu rule induct algorithm examin rule qualiti measur guid rule induct process reflect wide rang differ rule learn heuristicsresult algorithm extens test collect real surviv dataset compar stateoftheart surviv tree random surviv forest algorithm rule qualiti measur outperform kaplanmei estim perform least equal well treebas algorithmsconclus separateandconqu rule induct combin weight scheme effect techniqu build rulebas model surviv data accord predict accuraci competit treebas represent

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