Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic Ensemble Classifiers has particular importance. Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert software programs are utilized to achieve the stability goals of this research. Results: The proposed approach is useful as a preprocessing method before employing heuristic Ensemble Classifiers; i. e., first discover optimal levels of important parameters and then apply these parameters to heuristic Ensemble Classifiers to attain the best results. Another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the Pareto front is used as response variable. Finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic Ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method. Conclusion: This approach can analyze more parameters with less computational costs in comparison with previous works. This capability is one of the advantages of the proposed method.

Head and neck cancer (HNC) recurrence is ever increasing among Ghanaian men and women. Because not all machine learning Classifiers are equally created, even if multiple of them suite very well for a given task, it may be very difficult to find one which performs optimally given different distributions. The stacking learns how to best combine weak classifier models to form a strong model. As a prognostic model for classifying HNSCC recurrence patterns, this study tried to identify the best stacked Ensemble classifier model when the same ML Classifiers for feature selection and stacked Ensemble learning are used. Four stacked Ensemble models; in which first one used two base Classifiers: gradient boosting machine (GBM) and distributed random forest (DRF); second one used three base Classifiers: GBM, DRF, and deep neural network (DNN); third one used four base Classifiers: GBM, DRF, DNN, and generalized linear model (GLM); and fourth one used five base Classifiers: GBM, DRF, DNN, GLM, and Naïve bayes (NB) were developed, using GBM meta-classifier in each case. The results showed that implementing stacked Ensemble technique consisting of five base Classifiers on gradient boosted features achieved better performance than achieved on other feature subsets, and implementing this stacked Ensemble technique on gradient boosted features achieved better performance compared to other stacked Ensemble techniques implemented on gradient boosted features and other feature subsets used. Learning stacked Ensemble technique having five base Classifiers on GBM features is clinically appropriate as a prognostic model for classifying and predicting HNSCC patients’ recurrence data.

This paper focuses on a new hybrid machine learning model for classifying eye states from EEG signals by integrating traditional techniques with deep learning methods. Our Hybrid LSTM-KNN architecture employs KNN for classification and uses LSTM networks to extract features temporally. In addition, we perform extensive feature engineering, including statistical Z-test and IQR filtering, dimensionality reduction using PCA, and multivariate analysis to further model the performance. Moreover, an SVM-based unsupervised clustering approach is proposed to partition the EEG feature space, followed by Ensemble learning in each cluster to improve accuracy and robustness. Using the EEG Eye State Dataset for the first assessment, the Hybrid LSTM-KNN model recorded an accuracy of 87.2% without PCA. Further improvements through statistical filtering outperformed initial expectations, achieving a 6% rise in performance to 89.1% after outlier removal, 89.1% with Z-test (σ = 3), and 88.3% with IQR (1.5x). After applying PCA along with Ensemble learning post clustering, the final model exceeded expectations with an accuracy and F1 score of 96.8%, surpassing Ensemble Cluster-KNN and traditional models based on Ensemble Cluster-KNN, Logistic Regression, SVM, and Random Forest. The outcome demonstrates the robustness and noise-resilience of the model’s performance in practical real-time brain-computer interface and cognitive monitoring systems.

Breast cancer is one of the most common malignant tumors and the main cause of cancer death among women worldwide. The diagnosis of this type of cancer is a challenging problem in cancer diagnosis researches. Several research before have proved that Ensemble based machine learning Classifiers are able to detect breast cancer spot more accurate. However, the success of an Ensemble classifier highly depends on the choice of method to combine the outputs of the Classifiers into a single one. This paper proposes a novel Ensemble method that uses modified differential evolution (DE) algorithm generated weights to create Ensemble of Classifiers for improving the accuracy of breast cancer diagnosis. This paper proposes an Ensemble-based classifier to improve the accuracy of breast cancer diagnosis. As the performance of DE algorithm is strongly influenced by selection of its control parameters, local unimodal sampling (LUS) technique is used to find these parameters. The two most popular Classifiers support vector machine (SVM) and K-nearest neighbor (KNN) Classifiers are used in the Ensemble. The classification is then carried out using the majority vote of the Ensemble. The accuracy of the presented model is compared to other approaches from literature using standard dataset. The experimental results based on breast cancer dataset show that the proposed model outperforms other Classifiers in breast cancer abnormalities classification with 99. 46% accuracy.