Research on Decision Forest Learning Algorithm

  •  Limin Wang    
  •  Xiongfei Li    


Decision Forests are investigated for their ability to provide insight into the confidence associated with each prediction, the ensembles increase predictive accuracy over the individual decision tree model established. This paper proposed a novel “bottom-top” (BT) searching strategy to learn tree structure by combining different branches with the same root, and new branches can be created to overcome overfitting phenomenon.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

WJCI (2020): 0.439

Impact Factor 2020 (by WJCI): 0.247

Google Scholar Citations (March 2022): 6907

Google-based Impact Factor (2021): 0.68

h-index (December 2021): 37

i10-index (December 2021): 172

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