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Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees.

Alaa M Elsayad1,2, Ahmed M Nassef1,3, Mujahed Al-Dhaifallah4

  • 1Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, P.O. Box 54, Wadi Aldawaser 11991, Saudi Arabia.

International Journal of Environmental Research and Public Health
|December 16, 2020
PubMed
Summary

Predicting substance biodegradability is crucial for environmental safety. This study demonstrates that balanced random trees and boosted C5.0 decision trees effectively classify ready biodegradation, outperforming other models on unbalanced datasets.

Keywords:
C5.0 decision treeK-nearest neighborsQSARbiodegradable substancesdiscrimination analysismachine learningrandom treessupport vector machine

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Area of Science:

  • Environmental Science
  • Cheminformatics
  • Machine Learning

Background:

  • Non-degradable substances pose environmental and health risks.
  • Predicting biodegradability without expensive experiments is essential.
  • Quantitative Structure-Activity Relationship (QSAR) models offer solutions but struggle with unbalanced data.

Purpose of the Study:

  • To evaluate balanced random trees (RTs) and boosted C5.0 decision trees (DTs) for QSAR modeling of ready biodegradation.
  • To assess the models' effectiveness in handling unbalanced molecular descriptor datasets.
  • To compare their performance against existing classification methods.

Main Methods:

  • Utilized a two-dimensional molecular descriptor dataset from the UCI machine learning repository.
  • Implemented balanced RTs using balanced bootstrap samples.
  • Applied cost-sensitive learning for boosted C5.0 DT modeling.
  • Ranked molecular descriptors by contribution to classification.

Main Results:

  • The proposed balanced RTs and boosted C5.0 DT models demonstrated superior performance compared to Support Vector Machine (SVM), K-nearest neighbors (KNN), and Discrimination Analysis (DA).
  • Detailed classification metrics including accuracy, sensitivity, specificity, precision, F1 score, and ROC/AUROC were analyzed.
  • Molecular descriptors were successfully ranked based on their importance in the classification process.

Conclusions:

  • Balanced RTs and boosted C5.0 DTs are effective QSAR models for classifying ready biodegradation, especially with unbalanced data.
  • These models offer improved accuracy and generalization capabilities for environmental risk assessment.
  • The findings provide a more efficient approach to predicting substance biodegradability.