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2025Conference PaperSpringer

Algorithmic-Symphony: Machine Learning Classification Models Across Diverse Datasets for Performance Evaluation and Comparison

S Adithya Krishna, R Sreeram, Avishek Rauniyar, BM Reddy, S Sarath

International Conference on Information and Communication Technology for Competitive Strategies (ICTCS 2024)

Overview

Abstract

A systematic comparative analysis of machine learning classification models across diverse datasets and application domains. The chapter examines algorithm performance through the lens of accuracy, computational efficiency, interpretability, and generalization capability. By presenting a structured comparison framework across multiple dataset types, the work helps practitioners make informed algorithm selection decisions based on problem characteristics rather than popularity or convenience.

Keywords
Machine LearningClassificationComparative AnalysisData ScienceAlgorithm SelectionModel Evaluation
Research Areas
Machine LearningData ScienceAlgorithm Comparison

Technical Details

Technologies Used
PythonScikit-learnPandasNumPyMatplotlib
Datasets
  • Multiple benchmark datasets across classification tasks
Models
  • Decision Trees
  • Random Forest
  • SVM
  • Neural Networks
  • Gradient Boosting

My Contribution

Role

Co-author — comparative framework implementation, experiment execution, analysis, chapter writing.

Details

Co-author. Conducted comparative analysis of machine learning algorithms across multiple domains and datasets. Implemented evaluation framework for systematic algorithm comparison and contributed to chapter writing.

Challenges

Ensuring fair comparison across algorithms with fundamentally different assumptions
Controlling for hyperparameter tuning effects on comparative results

Key Contributions

Systematic framework for comparative ML algorithm evaluation across diverse datasets
Empirical analysis of algorithm performance by problem type
Practical algorithm selection guidelines for practitioners

Impact

Provides a structured, reproducible approach to algorithm selection that moves beyond intuition-based decisions to evidence-driven methodology.

Lessons Learned

No algorithm is universally superior — performance is deeply context-dependent
Hyperparameter tuning can reverse apparent algorithm rankings; fair comparison requires equal tuning effort
Simple models often match complex ones when data is limited — resisting complexity bias is a skill

Citation

APA

Krishna, S. A., Sreeram, R., Rauniyar, A., Reddy, B. M., & Sarath, S. (2025). Algorithmic-Symphony: Machine learning classification models across diverse datasets for performance evaluation and comparison. In Proceedings of ICTCS 2024, Lecture Notes in Networks and Systems (Vol. 1319, pp. 53–67). Springer. https://doi.org/10.1007/978-981-96-4145-1_5

IEEE

S. A. Krishna, R. Sreeram, A. Rauniyar, B. M. Reddy, and S. Sarath, "Algorithmic-Symphony: Machine Learning Classification Models Across Diverse Datasets for Performance Evaluation and Comparison," in Proc. Int. Conf. Inf. Commun. Technol. Competitive Strategies (ICTCS), 2024, LNNS, vol. 1319, pp. 53–67. Springer, 2025.

MLA

Krishna, S. Adithya, et al. "Algorithmic-Symphony: Machine Learning Classification Models Across Diverse Datasets for Performance Evaluation and Comparison." Proceedings of ICTCS 2024, LNNS, vol. 1319, Springer, 2025, pp. 53-67.

Chicago

Krishna, S. Adithya, R. Sreeram, Avishek Rauniyar, B. M. Reddy, and S. Sarath. "Algorithmic-Symphony: Machine Learning Classification Models Across Diverse Datasets for Performance Evaluation and Comparison." In Proceedings of ICTCS 2024, Lecture Notes in Networks and Systems, 1319:53–67. Springer, 2025. https://doi.org/10.1007/978-981-96-4145-1_5.

BibTeX
@inproceedings{krishna2025algorithmic,
  title     = {Algorithmic-Symphony: Machine Learning Classification Models Across Diverse Datasets for Performance Evaluation and Comparison},
  author    = {Krishna, S Adithya and Sreeram, R and Rauniyar, Avishek and Reddy, BM and Sarath, S},
  booktitle = {International Conference on Information and Communication Technology for Competitive Strategies (ICTCS 2024)},
  series    = {Lecture Notes in Networks and Systems},
  volume    = {1319},
  pages     = {53--67},
  publisher = {Springer},
  year      = {2025},
  doi       = {10.1007/978-981-96-4145-1_5}
}