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![Confusion matrix for multi-class classification [21]](publication/363099952/figure/tbl1/AS:11431281081753700@1661871639889/Confusion-matrix-for-multi-class-classification-21_Q320.jpg)
Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research.Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Fo...
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Context 1
... prediction results are compared with the actual data and loaded into the confusion matrix. Table 3 is the result of the confusion matrix on the proposed model. In Table 3, the prediction results for the negative class are 1137 data classified as a negative class, 25 data classified as a neutral class, and 69 data classified as the positive class. ...Context 2
... 3 is the result of the confusion matrix on the proposed model. In Table 3, the prediction results for the negative class are 1137 data classified as a negative class, 25 data classified as a neutral class, and 69 data classified as the positive class. The prediction result for the neutral class is 144 data classified as a neutral class, 79 data classified as a negative class, and 53 data classified as the positive class. ...Context 3
... prediction result for the positive class is 676 data classified as a positive class, 15 data classified as a neutral class, and 70 data classified as negative class. Based on the confusion matrix in Table 3, the accuracy, precision, recall, and f1-score values can be calculated, the results are shown in Figure 4. The results of the classification report in Figure 4 show that the stacking ensemble classifier model in this study has a good performance. ...Similar publications
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![Fig. 1. Basic structure of an RNN [19].](profile/Dante-Mujica-Vargas/publication/363661481/figure/fig1/AS:11431281085104183@1663605249377/Basic-structure-of-an-RNN-19_Q320.jpg)
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Citations
... Another method are Support Vector Machine (SVM) [14][15] [16], and Backpropagation Neural Network [17]. Naive Bayes has the advantage of being simple but has high accuracy even though it uses not a lot of data, while SVM has the advantage of solving linear and non-linear classification and regression problems which can become a learning algorithm capability for regression and classification, but the Support Vector Machine (SVM) are not efficient in training large-capacity data [18]. Backpropagation has advantages over other Neural Networks, namely Backpropagation using supervised training [19]. ...
Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
