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

Irad Ben-Gal*, Chavazelet Trister

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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' (SFGOTA) 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.

Original languageEnglish
Pages (from-to)64-78
Number of pages15
JournalApplied Stochastic Models in Business and Industry
Volume31
Issue number1
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Big data
  • Classification
  • Look-ahead algorithms
  • Online applications
  • Parallel computing
  • Recommendation systems

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