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@article{180455,
        author = {Nivedita Singh},
        title = {Enhancing Predictive Performance Through Label Noise Correction in Structured Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1373-1379},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180455},
        abstract = {Label noise is a common challenge in real-world structured datasets that can significantly degrade the performance of machine learning models. This paper explores the impact of noisy labels on classification accuracy and demonstrates how correcting mislabeled data can restore and enhance predictive performance. Using the Breast Cancer Wisconsin Diagnostic dataset with artificially introduced label noise, a cross-validation-based relabeling technique to identify and correct noisy labels. Experimental results show that models trained on the denoised dataset achieve accuracy comparable to or exceeding that of models trained on clean data, validating the effectiveness of noise correction in improving classification outcomes. This study highlights the importance of label quality in structured data and provides a practical workflow for mitigating noise, which can be applied to a wide range of data science applications.},
        keywords = {Label noise, Noise correction, Data denoising, Structured data, Classification accuracy, Machine learning, Cross-validation, Data quality, Predictive modeling, Supervised learning.},
        month = {June},
        }