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Random forest of perfect trees: concept, performance, applications and perspectives.

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This study introduces a novel random forest (RF) approach that builds error-free decision trees using artificial neurons. This new method, employing Nguyen information criteria (NICs), enhances feature selection and predictive modeling.

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Area of Science:

  • Machine Learning
  • Computational Biology
  • Statistical Modeling

Background:

  • Traditional Random Forests (RF) maximize tree variability by adhering to Breiman's principles.
  • This research proposes a novel RF that deviates from these principles by constructing numerous trees with zero classification errors.

Purpose of the Study:

  • To develop a new type of random forest (RF) that builds perfect classification trees.
  • To introduce a scoring system, the Nguyen Information Criteria (NICs), for evaluating feature predictive qualities.
  • To assess the performance of the new RF and NICs against existing methods.

Main Methods:

  • Utilized a novel decision tree structure incorporating neurons at each node and a unique "half Christmas tree" architecture.
  • Developed ten new statistical information criteria, termed Nguyen Information Criteria (NICs), for feature evaluation.
  • Implemented the "perfect tree" construction methods in the R package 'ROP'.

Main Results:

  • The first NIC demonstrated faster minimization of the Akaike information criterion compared to the Gini index in logistic regression.
  • Features selected using the NICScore showed a marginal advantage over Support Vector Machines-Recursive Feature Elimination (SVM-RFE).
  • Artificial neurons in tree nodes enable simultaneous consideration of multiple classifiers, leading to perfect trees without errors.

Conclusions:

  • The proposed RF method, utilizing artificial neurons and NICs, offers a powerful alternative for feature selection and predictive modeling.
  • The developed NICs provide an effective means to evaluate feature predictive quality.
  • The "perfect tree" approach in RFs can achieve high accuracy and overcome limitations of traditional methods.