Important complexity reduction of random forest in multi-classification problem

Kawther Hassine, Aiman Erbad, Ridha Hamila

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

45 Citations (Scopus)

Abstract

Algorithm complexity in machine learning problems has been a real concern especially with large-scaled systems. By increasing data dimensionality, a particular emphasis is placed on designing computationally efficient learning models. In this paper, we propose an approach to improve the complexity of a multi-classification learning problem in cloud networks. Based on the Random Forest algorithm and the highly dimensional UNSW-NB 15 dataset, a tuning of the algorithm is first performed to reduce the number of grown trees used during classification. Then, we apply an importance-based feature selection to optimize the number of predictors involved in the learning process. All of these optimizations, implemented with respect to the best performance recorded by our classifier, yield substantial improvement in terms of computational complexity both during training and prediction phases.

Original languageEnglish
Title of host publication2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-231
Number of pages6
ISBN (Electronic)9781538677476
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes
Event15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 - Tangier, Morocco
Duration: 24 Jun 201928 Jun 2019

Publication series

Name2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019

Conference

Conference15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019
Country/TerritoryMorocco
CityTangier
Period24/06/1928/06/19

Keywords

  • Algorithm complexity
  • Feature selection
  • Predictor importance
  • Random Forest

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