Discussions on the generalization of HybridSP on more equivalent benchmarks

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computational Modeling

Background:

• The HybridSP model's initial evaluation requires further methodological clarification.
• Assessing model generalization across diverse datasets is crucial for real-world applicability.

Purpose of the Study:

• To address reviewer concerns by enhancing methodological transparency and rigor.
• To provide a more comprehensive assessment of the HybridSP model's performance.
• To evaluate the model's robustness and generalization on new, diverse benchmarks.

Main Methods:

• Clarification of key methodological aspects of the HybridSP model.
• Inclusion of additional analytical data to support findings.
• Evaluation on expanded and fairer benchmark datasets beyond the original scope.

Main Results:

• Improved transparency and rigor in the HybridSP model's methodology.
• Demonstrated robustness and generalization ability of the HybridSP model on diverse benchmarks.
• Strengthened conclusions regarding the model's capabilities.

Conclusions:

• The HybridSP model exhibits enhanced performance and reliability after methodological clarifications and expanded evaluations.
• The additional analyses and benchmark testing confirm the model's robust generalization capabilities.
• The study provides a more comprehensive understanding of HybridSP's strengths and applicability.