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@article{198654,
        author = {Dr. Leena More, Deshmukh and Dr. Binod Kumar},
        title = {Hybrid Stacking-Based Framework for Talent Prediction and Top Performer Segmentation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12490-12494},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198654},
        abstract = {Talent prediction and workforce segmentation are critical challenges in modern HR analytics, especially in industrial environments involving blue-collar employees. This study proposes a hybrid ensemble framework using stacking techniques integrating Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. The model is evaluated on a structured HR dataset with 1000+ records. Results demonstrate superior predictive accuracy (91.3%), improved recall, and robust generalization. Visualization through ROC curves, confusion matrix, and feature importance further validates the model’s effectiveness.},
        keywords = {Talent Prediction, Stacking Ensemble, HR Analytics, Machine Learning, Hybrid Classifier, Workforce Segmentation},
        month = {April},
        }