Figure 2.6 A high-level representation of stacking.
2.8 Efficiency Analysis
The efficiency achieved by the considered ensemble machine learning techniques, i.e., Bayes optimal classifier, bagging, boosting, BMA, bucket of models, and tacking, is compared toward the performance metrics, i.e., accuracy, throughput, execution time, response time, error rate, and learning rate [30]. From the analysis, it is observed that the efficiency achieved by Bayesian model combination, stacking, and Bayesian model combination are high compared to other ensemble models considered for identification of zonotic diseases.
| Technique | Accuracy | Throughput | Execution time | Response time | Error rate | Learning rate |
| Bayes optimal classifier | Low | Low | High | Medium | Medium | Low |
| Bagging | Low | Medium | Medium | High | Low | Low |
| Boosting | Low | Medium | High | High | High | Low |
| Bayesian model averaging | High | High | Medium | Medium | Low | Low |
| Bayesian model combination | High | High | Low | Low | Low | High |
| Bucket of models | Low | Low | High | Medium | Medium | Low |
| Stacking | High | High | Low | Low | low | Medium |
2.9 Conclusion
This chapter provides introduction to zonotic diseases, symptoms, challenges, and causes. Ensemble machine learning uses multiple machine learning algorithms to identify the zonotic diseases in early stage itself. Detailed analysis of some of the potential ensemble machine learning algorithms, i.e., Bayes optimal classifier, bootstrap aggregating (bagging), boosting, BMA, Bayesian model combination, bucket of models, and stacking are discussed with respective architecture, advantages, and application areas. From the analysis, it is observed that the efficiency achieved by Bayesian model combination, stacking, and Bayesian model combination are high compared to other ensemble models considered for identification of zonotic diseases.
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