International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Data Governance-Driven Trust Scoring Framework and Its Impact on Machine Learning Performance |
|---|---|
| ABSTRACT | The reliability of machine learning systems depends heavily on data trustworthiness. Traditional models assume equal reliability across observations despite missing values, duplicates, invalid entries, and label corruption. This research proposes a Data Governance-Driven Trust Scoring Framework integrated with Trust-Aware Adaptive Learning (TAAL) to evaluate and improve predictive robustness under degraded data conditions. Experimental evaluation using Random Forest and Logistic Regression demonstrated measurable performance degradation under low-trust data and modest but consistent improvements through trust-aware adaptive weighting. |
| AUTHOR | TIRTH RAVAL, DR. MANISHA BHARATI M.Sc. Data Science Student, Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India Guide, Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405098 |
| pdf/98_Data Governance-Driven Trust Scoring Framework and Its Impact on Machine Learning Performance.pdf | |
| KEYWORDS | |
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