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Related Experiment Video

Updated: Mar 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Random subspace-based ensemble classifier for high-dimensional data Using SPARK.

Venkaiah Chowdary Bhimineni1, Rajiv Senapati1

  • 1Department of CSE, SRM University, AP, Amaravati, Mangalagiri, Andhra Pradesh, India.

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|March 11, 2026
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Summary

This study introduces an improved subspace-based ensemble classifier (ISSBEC) to tackle challenges in high-dimensional data classification. The novel approach enhances accuracy and robustness for machine learning models dealing with sparse, high-dimensional datasets.

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Science

Background:

  • High-dimensional data classification faces challenges like sparsity and overfitting due to the 'curse of dimensionality'.
  • Increasing features lead to data sparsity, hindering classification generalization, increasing computational costs, and reducing accuracy.
  • Existing methods struggle with the scalability and performance on datasets with numerous features.

Purpose of the Study:

  • To propose a novel ensemble classifier designed to overcome the limitations of high-dimensional data classification.
  • To enhance the accuracy, robustness, and scalability of machine learning models in high-dimensional settings.
  • To introduce a framework that effectively handles feature selection and fusion for improved classification performance.

Main Methods:

  • Data normalization using min-max normalization.
  • Data partitioning via improved deep fuzzy clustering (IDFC) on the master node.
  • Feature selection using support vector machine-modified recursive feature elimination (SVM-MRFE) and feature fusion on slave nodes.
  • An improved subspace-based ensemble classifier (ISSBEC) integrating feature-fusion-based random subspace (FF-RSS), mixed-space enhancement (MSE), and multiple base classifiers.

Main Results:

  • The proposed ISSBEC classifier demonstrated improved accuracy and robustness compared to state-of-the-art methods.
  • Experimental results validated the effectiveness of the proposed approach in handling high-dimensional datasets.
  • The framework offers a scalable solution for machine learning tasks involving large feature sets.

Conclusions:

  • The ISSBEC classifier provides an effective solution for high-dimensional data classification challenges.
  • The proposed methods for feature selection, fusion, and ensemble classification significantly improve model performance.
  • The Spark-based implementation ensures scalability and efficiency for real-world applications.