A Hybrid Intelligent Approach for Predicting Digital Transformation Change Levels Based on Stacked Ensemble Learning and Deep Meta-Learning
Keywords:
Level of changes in digital transformation, Feature selection, Binary Archimedes Optimization Algorithm, Stacked ensemble learning, Deep meta-learningAbstract
This study aimed to develop an intelligent framework based on feature selection and stacked deep learning to predict the level of digital transformation changes in organizations and improve the accuracy of digital transformation classification. The study utilized a standard corporate digital transformation dataset containing 2,000 samples and 24 features. In the first stage, influential features were extracted using the Binary Archimedes Optimization Algorithm (BAOA) and a wrapper-based feature selection approach. The selected features were then fed into a stacked ensemble and deep meta-learning framework (SE-DML). The architecture consisted of four base classifiers, including Random Forest, AdaBoost, XGBoost, and Bagging, while a multilayer deep neural network acted as the meta-learner. The dataset was divided into training (70%) and testing (30%) subsets using stratified sampling. Model performance was evaluated using Accuracy, Balanced Accuracy, Precision, Recall, F1-Score, Kappa, MCC, Log-Loss, AUROC, and AUPRC metrics across 20 independent runs. The results demonstrated that the BAOA algorithm successfully identified stable and influential digital transformation features while achieving rapid convergence. Feature frequency analysis indicated that the variable “digitaltransindex” had the greatest contribution to predicting digital transformation levels. The proposed hybrid model achieved outstanding classification performance. Among all classifiers, Bagging achieved the best results with an Accuracy of 0.9995, F1-score of 0.9994, and the lowest Log-Loss value of 0.0048. The STACK model based on deep meta-learning also showed highly reliable performance with an Accuracy of 0.9985 and an AUROC value of 1.0. Furthermore, MCC and Kappa values confirmed the strong discriminative capability of the proposed framework in identifying digital transformation levels. The findings revealed that integrating BAOA-based feature selection with stacked ensemble learning and deep meta-learning provides an effective framework for predicting digital transformation change levels. The proposed approach successfully reduced data dimensionality, eliminated redundant features, and extracted complex organizational patterns with high predictive accuracy. The model outperformed individual classifiers and demonstrated strong potential for application in managerial decision-support systems and strategic digital transformation planning.
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Copyright (c) 2025 Samaneh Hedayati (Author); Seyed Javad Iranbanfard (Corresponding author); Sara Najafzadeh, Mostafa Kolahdoozi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
