Add to ORKGThe experiment\u27s result shows the average error rate of that the REP algorithm will produce the smallest error rate. Although the error rate of REP algorithm is the smallest, the difference value between ERP\u27s and EBP\u27s error rate is only 0.5%. Even though they have almost similar error rate, EBP algorithm proposes more simple decision tree than REP algorithm does
<p>Panel A shows the difference between REPT and MOCS on the horizontal axis and the difference betw...
<p>Error rate (M, SD in %) in Experiments 1–4 as a function of key distance (close, far) and Stroop ...
Abstract. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared...
a<p>Convergence steps: the iteration steps when the algorithm is converged.</p>b<p>Cluster Number: t...
Performance comparison of recent and effective algorithms for minimizing error-based cost functions....
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>Performance comparison of the proposed algorithm and 17 existing algorithms using four existing e...
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Performances of ImpAO, AO, SMA, MFO and ABC algorithms for minimization of different error-based cos...
Decision Tree (DT) typically splitting criteria using one variable at a time. In this way, the final...
<p>Predicted accuracy in the repeated binary choice experiment depending on the frequency of the maj...
In this paper we explore several issues relevant to the benchmarking and comparison of machine learn...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
Abstract: Decision Tree is a classification method used in Machine Learning and Data Mining. One maj...
Equal error rate (EER) and correct classification ratio (CCR) of MSM-based method [11] and the B-2D-...
<p>Panel A shows the difference between REPT and MOCS on the horizontal axis and the difference betw...
<p>Error rate (M, SD in %) in Experiments 1–4 as a function of key distance (close, far) and Stroop ...
Abstract. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared...
a<p>Convergence steps: the iteration steps when the algorithm is converged.</p>b<p>Cluster Number: t...
Performance comparison of recent and effective algorithms for minimizing error-based cost functions....
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>Performance comparison of the proposed algorithm and 17 existing algorithms using four existing e...
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Performances of ImpAO, AO, SMA, MFO and ABC algorithms for minimization of different error-based cos...
Decision Tree (DT) typically splitting criteria using one variable at a time. In this way, the final...
<p>Predicted accuracy in the repeated binary choice experiment depending on the frequency of the maj...
In this paper we explore several issues relevant to the benchmarking and comparison of machine learn...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
Abstract: Decision Tree is a classification method used in Machine Learning and Data Mining. One maj...
Equal error rate (EER) and correct classification ratio (CCR) of MSM-based method [11] and the B-2D-...
<p>Panel A shows the difference between REPT and MOCS on the horizontal axis and the difference betw...
<p>Error rate (M, SD in %) in Experiments 1–4 as a function of key distance (close, far) and Stroop ...
Abstract. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared...