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Parallel construction of decision trees with consistently non‐increasing expected number of tests

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  • Irad Ben‐Gal
  • Chavazelet Trister

Abstract

In recent years, with the emergence of big data and online Internet applications, the ability to classify huge amounts of objects in a short time has become extremely important. Such a challenge can be achieved by constructing decision trees (DTs) with a low expected number of tests (ENT). We address this challenge by proposing the ‘save favorable general optimal testing algorithm’ (SF‐GOTA) that guarantees, unlike conventional look‐ahead DT algorithms, the construction of DTs with monotonic non‐increasing ENT. The proposed algorithm has a lower complexity in comparison to conventional look‐ahead algorithms. It can utilize parallel processing to reduce the execution time when needed. Several numerical studies exemplify how the proposed SF‐GOTA generates efficient DTs faster than standard look‐ahead algorithms, while converging to a DT with a minimum ENT.

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  • Irad Ben‐Gal & Chavazelet Trister, 2015. "Parallel construction of decision trees with consistently non‐increasing expected number of tests," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 64-78, January.
    • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:1:p:64-78
    DOI: 10.1002/asmb.2086
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    1. Lorenzo Ricciardi Celsi & Andrea Caliciotti & Matteo D'Onorio & Eugenio Scocchi & Nour Alhuda Sulieman & Massimo Villari, 2021. "On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks," Future Internet, MDPI, vol. 13(10), pages 1-16, October.

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