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This study introduces a novel PSO-BOA-KELM algorithm for multi-label classification, improving Kernel Extreme Learning Machine (KELM) performance. It efficiently optimizes KELM parameters, enhancing prediction accuracy for complex data streams.

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Kernel Extreme Learning Machine (KELM) is effective for batch multi-label classification due to efficient processing and performance.
  • Data streams in practical applications present challenges like vastness, speed, multi-labeling, and concept drift, impacting accuracy and space-time complexity.
  • KELM training requires repetitive independent runs to optimize generalization performance and hidden layer nodes, posing computational challenges.

Purpose of the Study:

  • To propose an optimized Kernel Extreme Learning Machine (KELM) multi-label classification method.
  • To address the limitations of traditional KELM training, specifically the need for repeated independent runs to optimize generalization and hidden layer nodes.
  • To enhance the prediction accuracy and efficiency of KELM for dynamic data streams.

Main Methods:

  • A novel Kernel Extreme Learning Machine multi-label data classification method is proposed, integrating the Butterfly Algorithm (BA) optimized by Particle Swarm Optimization (PSO).
  • The proposed PSO-BOA-KELM algorithm optimizes both model generalization ability and the number of hidden layer nodes.
  • The method enables simultaneous training of multiple KELM hidden layer networks, maintaining current time complexity and reducing repeated calculations.

Main Results:

  • The PSOBOA-KELM algorithm demonstrates superior performance compared to PSO-KELM, BBA-KELM, and BOA-KELM.
  • It more effectively searches for optimal Kernel Extreme Learning Machine parameters.
  • The algorithm achieves a better balance between global and local performance, leading to a KELM prediction model with higher prediction accuracy.

Conclusions:

  • The proposed PSO-BOA-KELM algorithm effectively optimizes KELM for multi-label classification tasks.
  • This method addresses the computational inefficiencies of traditional KELM training for dynamic data streams.
  • The enhanced KELM model offers improved prediction accuracy and parameter optimization capabilities.