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Evolving novel classification measures, optimising and evaluating the weights in weighted average random forest using quadratic programming

Kala Nisha Gopinathan et al.

International Journal of Enterprise Network Management2025https://doi.org/10.1504/ijenm.2025.151291article
AJG 1ABDC B
Weight
0.50

Abstract

Attribute selection measures are used in decision trees to select the feature that best splits the data into homogeneous parts. There are four existing measures: the Gini index, entropy, information gain, and gain ratio. In this paper, two novel attribute measures were proposed and were tested on 10 different datasets using decision trees to find their effectiveness. Statistical tests conducted on the proposed novel attribute selection measures showed that they were able to achieve the same level of performance as the existing measures, while eliminating the limitations in the existing measures. In the weighted average random forest, the weights were optimised by minimising the objective function of mean squared error (MSE). This has been done through Quadratic Programming, and the computed optimal weights were used with the decision trees in the weighted average random forest. Overall, it was determined that the weighted average random forest outperformed the random forest.

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https://doi.org/https://doi.org/10.1504/ijenm.2025.151291

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@article{kala2025,
  title        = {{Evolving novel classification measures, optimising and evaluating the weights in weighted average random forest using quadratic programming}},
  author       = {Kala Nisha Gopinathan et al.},
  journal      = {International Journal of Enterprise Network Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijenm.2025.151291},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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