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Effective hybrid feature selection using different bootstrap enhances cancers classification performance.

Noura Mohammed Abdelwahed1, Gh S El-Tawel2, M A Makhlouf3

  • 1Department of Information Systems, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt. malekmalek20131988@gmail.com.

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Summary
This summary is machine-generated.

This study introduces a new machine learning method, positions first bootstrap step random forest selection recursive feature elimination (PFBS-RFS-RFE), to improve cancer classification accuracy. The PFBS-RFS-RFE method enhances feature selection for high-dimensional data, overcoming limitations of existing algorithms.

Keywords:
High dimensional dataLearning algorithmsMachine LearningOver-fittingRandom Forest feature importanceRecursive feature elimination and its disadvantages

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Oncology

Background:

  • Machine learning aids in predicting human cancer onset, but high-dimensional data presents challenges like overfitting and long computation times.
  • Recursive Feature Elimination (RFE) is effective for feature selection but suffers from computational expense and overfitting.
  • Random Forest for Selection (RFS) improves feature selection and mitigates overfitting.

Purpose of the Study:

  • To propose a novel method, PFBS-RFS-RFE, to enhance cancer classification performance by improving feature selection.
  • To address the limitations of RFE, including computational time and overfitting, in high-dimensional cancer datasets.
  • To evaluate the effectiveness of different bootstrap strategies within the PFBS-RFS-RFE framework.

Main Methods:

  • The study introduces positions first bootstrap step random forest selection recursive feature elimination (PFBS-RFS-RFE) with three bootstrap positions: outer first bootstrap step (OFBS), inner first bootstrap step (IFBS), and outer/inner first bootstrap step (O/IFBS).
  • RFS is employed for feature importance, which is then combined with RFE using logistic regression as the estimator.
  • The proposed methods were integrated with four classifiers and assessed on five diverse datasets.

Main Results:

  • The outer/inner first bootstrap step (O/IFBS) within the PFBS-RFS-RFE framework demonstrated superior performance compared to prior methods.
  • Significant enhancements in accuracy, variance, and ROC area were observed for RNA gene and dermatology erythemato-squamous diseases datasets.
  • Specific improvements included achieving 99.994% accuracy and a 1.000 ROC area for RNA gene data, and 100.000% accuracy with a 1.000 ROC area for dermatology data.

Conclusions:

  • The PFBS-RFS-RFE method effectively addresses challenges in cancer classification performance associated with high-dimensional data and the RFE algorithm.
  • The integration of RFS-extracted features with RFE, utilizing different bootstrap positions, yields improved feature selection and classification accuracy.
  • The proposed approach offers a robust solution for enhancing cancer classification in complex, high-dimensional biological datasets.