How do you diagnose poor lift performance on an SVM classifier?
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| Papers (14) | Insight |
|---|---|
16 Citations | Our method was tested and verified on various UCI repository datasets and the results indicate that this method speeds up the learning phase of SVM without losing any generality or affecting the final model of classifier. |
| The empirical assessment conducted on four benchmark collections evidence that proposed method performs comparably to state-of-the-art SVM classifier in classifying performance, as well as beats it in running time. | |
01 Dec 2012 22 Citations | The multi-class SVM classifier with RBF kernel has shown superior classification performance. |
| It is observed that SVM classifier produces better percentage of accuracy in classification. | |
| Results demonstrate that the combination of EMD and SVM can be an efficient classifier with acceptable levels of accuracy. | |
| SVM exhibit a good performance as classifier despite similitude between some disturbance patterns. | |
01 Dec 2016 | Improved performance measure shows satisfactory results upon application of SVM. |
| Compared to single SVMs, the multi-SVM classification system exhibits promising accuracy performance on well-known data sets. | |
177 Citations | Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers. |
| Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers. | |
01 Jan 2005 27 Citations | The obtained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature sets. |
01 Jun 2010 | In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion rules. |
| Furthermore, our results suggest that in this study the increase of the classification performance due to the weighting is greater than that obtained by selecting the underlying classifier or the kernel part of the SVM. | |
06 Mar 2010 80 Citations | Finally, experimental data show that F1 value of SVM classifier has reached more than 86.26%, and the classification results comparing to other classification methods have greatly improved, and it also proves that SVM is an effective machine learning method. |
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