Survival analysis of automobile components using mutually exclusive forests

Ayelet Eyal, Lior Rokach, Meir Kalech, Ofra Amir, Rahul Chougule, Rajkumar Vaidyanathan, Kallappa Pattada

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

An ability to predict the mileage at failure of components in a complicated system, particularly in automobiles, is a challenging task. In the current work, a methodology for estimating the distribution of failure and survival rate of automobile components affected by multiple factors is presented. A novel adaptation of an ensemble recursive partitioning and tree-based learning method, mutually exclusive forest, is introduced. The proposed method is capable of handling a high dimensional dataset and maximizing the extracted information to estimate the distribution of mileage at failure of automobile components. Each tree in the proposed mutually exclusive forest uses a mutually exclusive set of factors in each of its constituent decision trees to classify the failure data. Information across the trees is combined to obtain the failure rate distribution of an automobile component with respect to mileage. A case study, based on real-world field failure data and censored data of automobile components, is presented to evaluate the proposed algorithm. Results show similar results to the C-Forest approach in terms of prediction quality, while generating models with significantly lower space that are easier to interpret.

Original languageEnglish
Article number6514923
Pages (from-to)246-253
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume44
Issue number2
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Classification and regression trees (CART)
  • conditional inference
  • ensemble algorithms
  • machine learning
  • random forests
  • survival analysis
  • survival trees

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