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<p>Training data optimization, also known as instance selection, is a critical preprocessing step in machine learning that enhances model performance by removing redundant or noisy data, reducing computational cost, and improving the handling of class imbalances. It ensures that the most informative and representative examples are retained, optimizing both the training process and the generalizability of the model. In this paper, we introduce a novel instance selection method designed to optimize the efficiency and effectiveness of machine learning models by enhancing the training dataset’s quality without resorting to artificial data manipulation. This method strategically selects representative instances that preserve the intrinsic value of the data, addressing challenges such as computational demand, noise, redundancy, and class imbalance. By incorporating adaptive clustering and a mechanism that adjusts the selection process based on the degree of class imbalance, our method offers a robust solution suitable for both balanced and imbalanced datasets. Extensive evaluations on numerous datasets demonstrate the competitiveness of our approach while maintaining computational efficiency.</p>

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Training Data Optimization for Classification via Weighted Dimensionality Reduction and Adaptive Stratified Clustering for Balanced and Imbalanced Data

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Hieu Pham,
  • Gaurav Arwade,
  • Yi Tan,
  • Paras Bhatt,
  • Vasileios Pavlopoulos

摘要

Training data optimization, also known as instance selection, is a critical preprocessing step in machine learning that enhances model performance by removing redundant or noisy data, reducing computational cost, and improving the handling of class imbalances. It ensures that the most informative and representative examples are retained, optimizing both the training process and the generalizability of the model. In this paper, we introduce a novel instance selection method designed to optimize the efficiency and effectiveness of machine learning models by enhancing the training dataset’s quality without resorting to artificial data manipulation. This method strategically selects representative instances that preserve the intrinsic value of the data, addressing challenges such as computational demand, noise, redundancy, and class imbalance. By incorporating adaptive clustering and a mechanism that adjusts the selection process based on the degree of class imbalance, our method offers a robust solution suitable for both balanced and imbalanced datasets. Extensive evaluations on numerous datasets demonstrate the competitiveness of our approach while maintaining computational efficiency.