A Weighted Majority Voting Ensemble Approach for Classification

Dogan, Alican; Birant, Derya

doi:10.1109/ubmk.2019.8907028

Cited by 73 publications

(38 citation statements)

References 13 publications

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“…The experiment was conducted using a tool known as RM, which is a common software developed for data science and machine learning tasks [33]. This tool is widely selected and used by researchers around the world based on its range of capabilities and easy application [65][66][67]. In the previous section, the description and details of the selected dataset were presented.…”

Section: Model Implementation Using Rmmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Section: Model Implementation Using Rmmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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“…How to accurately allocate large weight to the base classifier with large contribution is also a key problem. Alican Dogan [17] proposed a new weight allocation method from the perspective of weight allocation. Different from the existing literature, this method only provides the reward mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…each base classifier on the training set, runs the trained classifier on the validation set, and obtains different weights [17], i.e. 1 ,..., ,...,…”

mentioning

confidence: 99%

The Optimization of Multi-classifier Ensemble Method Based on Dynamic Weighted Voting

Yang

Fang²,

et al. 2022

J. Phys.: Conf. Ser.

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Generally, on the same data set, different deep learning classification models will achieve different performances. The existing weighted voting method can combine the results of models, which can improve the performance of classification. On the other side, its classification accuracy is affected by the accuracy of all models. In this paper, we proposed a dynamic weighted voting method. Our method dynamically selects models on different data sets, and integrates them according to their weights, thereby improving the classification accuracy. We evaluated the methods on three data sets of CIFAR10, CIFAR100 and Existing, which increased the accuracy about 0.65%, 0.91%, and 0.78% respectively compared with the existing weighted voting method.

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“…There are also some variations of these methods. For instance, Dogan and Birant [6] proposed initializing all the weights to the same value, and reward the best performing trees on the validation set formed by the out-of-bag samples. Zhukov et al [7] added a pruning step to replace the worst decision tree so the ensemble can handle concept drift.…”

Section: Related Workmentioning

confidence: 99%

Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

Pascual-Fontanilles¹,

Valls²,

Moreno³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Random Forests are well-known Machine Learning classification mechanisms based on a collection of decision trees. In the last years, they have been applied to assess the risk of diabetic patients to develop Diabetic Retinopathy. The results have been good, despite the unbalance of data between classes and the inherent ambiguity of the problem (patients with similar data may belong to different classes). In this work we propose a new iterative method to update the set of trees in the Random Forest by considering trees generated from the data of the new patients that are visited in the medical centre. With this method, it has been possible to improve the results obtained with standard Random Forests.

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A Weighted Majority Voting Ensemble Approach for Classification

Cited by 73 publications

References 13 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

The Optimization of Multi-classifier Ensemble Method Based on Dynamic Weighted Voting

Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

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