Ensemble learning involves using several individual classifiers and combining their predictions, which may result in better performance than a single classifier. This article proposes a two-layer flexible voting ensemble model to predict the customer churn rates in the telecommunication industries. The datasets used in this study are from IBM Sample Data Sets and Duke University. Following the pre-processing stage, the datasets have been categorized into an imbalanced and a balanced set. The balanced set comprises of an equal number of instances for both classes ('churn' and 'not churn').Extensive investigations were also carried out to determine the circumstances under which the model provides the best performance. The results of the hybrid algorithm with the IBM imbalanced dataset give an accuracy of 82.30% and an F1-score of 63%. However, with the IBM balanced dataset, an accuracy of 76.20% and an F1-score of 77.06% are obtained. When considering the dataset from Duke University an accuracy of 71.33% and an F1-score of 14.3% are obtained with the imbalanced dataset. The proposed model provides an accuracy of 60.41% and an F1-score of 64.13% with the corresponding balanced dataset. Test work results indicate that the approach adopted has significantly increased the F1-score of the classification when considering a balanced dataset in both cases. Additionally, p-values of less than 0.05 indicate that the results obtained with IBM imbalanced dataset and both balanced and imbalanced dataset from Duke University are statistically significant.
This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding.
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