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Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition.

Shaoxia Mou1, Heming Zhang2

  • 1University of Perpetual Help System Dalta, Graduate School Eternal University, Las Piñas, Philippines.

Plos One
|July 7, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel algorithm for mining unbalanced data cumulative sequences by generating new samples to balance the data. This approach enhances mining performance and accuracy, optimizing results for better data analysis.

Area of Science:

  • Data Mining and Machine Learning
  • Artificial Intelligence
  • Statistical Analysis

Background:

  • Unbalanced data cumulative sequences present challenges in data mining due to a large number of categories, often leading to performance degradation.
  • Existing methods struggle to effectively handle the inherent imbalance in cumulative sequence data, impacting the accuracy of mining results.

Purpose of the Study:

  • To optimize the performance of data cumulative sequence mining for unbalanced datasets.
  • To develop an algorithm that effectively balances cumulative sequences and improves the accuracy of data mining.

Main Methods:

  • A novel algorithm for mining unbalanced data cumulative sequences based on probability matrix decomposition.
  • Determining natural nearest neighbors for few samples, clustering them, and generating new samples to balance the sequence.

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  • Utilizing probability matrix decomposition with Gaussian distributed random matrices and AdaBoost for adaptive sample weighting.

    • Main Results:

    • The algorithm effectively generates new samples, significantly improving the balance of data cumulative sequences.
    • Probability matrix decomposition, combined with AdaBoost, optimizes global and single-sample errors, achieving minimum RMSE at a decomposition dimension of 5.
    • The proposed algorithm demonstrates superior classification performance on balanced data cumulative sequences, with top average rankings for F-value, G-mean, and AUC.

    Conclusions:

    • The developed algorithm successfully addresses the challenges of unbalanced data cumulative sequences, leading to more accurate mining outcomes.
    • The method provides a robust approach for data balancing and enhances the overall effectiveness of sequence mining techniques.
    • The findings suggest significant improvements in data mining performance and classification accuracy through the proposed balancing and decomposition strategy.