# An Ensemble Extreme Learning Machine for Data Stream Classification
## 4 Experiments and Data Analysis
### The Comparison Results of CELM and Comparison Algorithms on the Test Datasets  
To test the performance of CELM and comparison algorithms, the algorithms are executed on 10 datasets. The test results are shown in Tables 2 and 3.  
Tables 2 and 3 show that CELM gets best results on four datasets; SEA, AE and OS-ELM get the best results on two datasets; and M_ID4 gets only one best result. In addition, the average accuracy of CELM is also the best of all. For time consumption, OS-ELM is the least of all and CELM the second least, but the accuracies of CELM are much higher than OS-ELM. Thus, it can be concluded that the performance of CELM is better than the other algorithm in most conditions. On _Ozone_ dataset, CELM and OS-ELM get the same highest accuracy because _Ozone_ has no abrupt concept drift and CELM degenerates into OS-ELM; in other words, there will be no difference between CELM and OS-ELM when dataset has no abrupt concept drift.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Size** & **Attributes** & **Classes** & **Types** \\ \hline voice & 7614 & 385 & 12 & Numeric \\ waveform & 50,000 & 21 & 3 & Numeric \\ bank & 45,211 & 16 & 2 & Mixed \\ adult & 32,561 & 13 & 2 & Mixed \\ letter & 20,000 & 16 & 26 & Categorical \\ hyperplane & 50,000 & 40 & 2 & Numeric \\ occupancy & 8143 & 5 & 2 & Numeric \\ hill & 1212 & 100 & 2 & Numeric \\ Protein & 1080 & 80 & 8 & Mixed \\ Ozone & 2534 & 72 & 2 & Numeric \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information of the experimental datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **CELM** & **SEA** & **AE** & **OS-ELM** & **M\_ID4** & _winsize_ & \(d\) & \(L\) \\ \hline voice & **0.6511 \(\pm\) 0.786** & 0.3357 \(\pm\) 0.0651 & 0.4155 \(\pm\) 0.0737 & 0.2808 \(\pm\) 0.0429 & 0.6029 \(\pm\) 0.1033 & 100 & 5 & 20 \\ waveform & 0.6619 \(\pm\) 0.0119 & 0.6329 \(\pm\) 0.0136 & 0.6374 \(\pm\) 0.0204 & **0.6856**\(\pm\) **0.0134** & 0.6205 \(\pm\) 0.0195 & 1000 & 200 & 200 \\ bank & **0.8863 \(\pm\) 0.0094** & 0.8841 \(\pm\) 0.0088 & 0.8843 \(\pm\) 0.0084 & 0.8812 \(\pm\) 0.0087 & 0.8269 \(\pm\) 0.0196 & 1200 & 13 & 5 \\ adult & 0.7596 \(\pm\) 0.0135 & 0.8119 \(\pm\) 0.0168 & **0.8156**\(\pm\) **0.0117** & 0.7569 \(\pm\) 0.0177 & 0.7501 \(\pm\) 0.0235 & 1000 & 5 & 5 \\ letter & 0.4930 \(\pm\) 0.0581 & 0.0361 \(\pm\) 0.0096 & 0.3617 \(\pm\) 0.0597 & 0.0418 \(\pm\) 0.0046 & **0.6818**\(\pm\) **0.1368** & 1000 & 13 & 2000 \\ hyperplane & **0.8512 \(\pm\) 0.0205** & 0.5761 \(\pm\) 0.0164 & 0.5758 \(\pm\) 0.0182 & 0.5796 \(\pm\) 0.0306 & 0.5385 \(\pm\) 0.0199 & 1000 & 30 & 2000 \\ occupancy & 0.9670 \(\pm\) 0.0294 & **0.9882**\(\pm\) **0.0111** & 0.9788 \(\pm\) 0.0191 & 0.7835 \(\pm\) 0.0291 & 0.9640 \(\pm\) 0.0182 & 100 & 5 & 10 \\ hill & 0.5517 \(\pm\) 0.0659 & **0.5643**\(\pm\) **0.0813** & 0.4833 \(\pm\) 0.0491 & 0.4900 \(\pm\) 0.0344 & 0.5283 \(\pm\) 0.0369 & 60 & 80 & 100 \\ Protein & 0.6354 \(\pm\) 0.0599 & 0.5750 \(\pm\) 0.1578 & **0.6589**\(\pm\) **0.1532** & 0.1354 \(\pm\) 0.0348 & 0.5854 \(\pm\) 0.1125 & 80 & 50 & 1000 \\ _Ozone_ & **0.9408**\(\pm\) **0.0107** & 0.9396 \(\pm\) 0.0251 & 0.9392 \(\pm\) 0.0131 & **0.9408**\(\pm\) **0.0107** & 0.7692 \(\pm\) 0.1018 & 120 & 30 & 200 \\ \hline Average & **0.7128**\(\pm\) **0.0297** & 0.6344 \(\pm\) 0.0406 & 0.6750 \(\pm\) 0.0424 & 0.5576 \(\pm\) 0.0227 & 0.8686 \(\pm\) 0.0592 & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: The test accuracies of the algorithms on the experimental datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **CELM** & **SEA** & **AE** & **OS-ELM** & **M\_ID4** \\ \hline voice & 2.0433 & 2401.3555 & 291.9706 & 0.1160 & 5234.9994 \\ waveform & 82.9626 & 1523.1448 & 172.0207 & 0.1480 & \(>\)20,000 \\ bank & 10.8994 & 573.6019 & 63.8236 & 0.1100 & 2392.9633 \\ adult & 6.1201 & 279.0587 & 37.7094 & 0.0566 & 2830.5794 \\ letter & 7