Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support

Shin Jye Lee, Ching Hsun Tseng, Hui Yu Yang, Xin Jin, Qian Jiang, Bin Pu, Wei Huan Hu, Duen Ren Liu, Yang Huang, Na Zhao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring. To address this concern, the data rotational method by PCA in tree‐based methods shows a path. This work tries to enhance this path by proposing an ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost. The random rotation process has replaced the manual pre‐defined number of subset features (free pre‐defined process). Therefore, with the ensemble of the multiple AdaBoost-based classifier, overfitting problems can be avoided, thus reinforcing the robustness. In our experiments with real‐world medical data sets, Random Rot- Boost reaches better classification performance when compared with existing methods. Thus, with the help from our proposed method, the quality of clinical decisions can potentially be enhanced and supported in medical tasks.

Original languageEnglish
Article number617
JournalEntropy
Volume24
Issue number5
DOIs
StatePublished - May 2022

Keywords

  • AdaBoost
  • Rotation Forest
  • classification
  • clinical decision support

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