# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
## 4 Experiments
### Results on Transfer Task: \(\text{AD}\to\text{MCI}\)  
177 AD and 40 NC subjects were grouped as training data; 245 MCI and 45 remaining NC subjects were combined as testing data. Since some studies indicated the possibility of inadequate classification due to imbalance sample sizes, we divided AD data into four groups, MCI data into two groups, and NC data into three groups, details of each group were shown in Table 4 (the last group in AD was AD which contained all 177 AD subjects). In this way, we set AD1 and NC1, AD2 and NC1, AD3 and NC1, and AD4 and NC1 as Src, respectively; MCI1 and NC3 as Tartrain; and, MCI2 and NC2 as Tartest. Then, the sample sizes of MCI1 and NC3 would be increased in the following experiments, as we exam the assumption of consistency between the size of Tartrain and the accuracy of classification. The proposed transfer learning method was compared with four classic classification algorithms, i.e., SVM, KNN, ITML, and Linear-MSVM. For the sake of fairness, the same Src, Tartrain, and Tartest were used in all methods. The classification performances based on all five methods are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Data Set**} \\ \hline Group & Sample size & Group & Sample size \\ \cline{2-5}  & AD1 & 40 & MCI (245) & MCI1 & 10 \\ \cline{2-5} AD (177) & AD2 & 40 & MCI2 & 235 \\ \cline{2-5}  & AD3 & 40 & NC1 & 40 \\  & AD4 & 57 & NC (85) & NC2 & 35 \\  & AD4 & 57 & NC3 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed information of data setting.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Groups** & **KNN** & **SVM** & **ITML** & **Linear MSVM** & **TrAdaboost** \\ \hline Group 1 & 0.385 & 0.671 & 0.563 & 0.510 & 0.752 \\ Group 2 & 0.348 & 0.692 & 0.455 & 0.510 & 0.752 \\ Group 3 & 0.359 & 0.7037 & 0.470 & 0.51 & 0.785 \\ Group 4 & 0.3481 & 0.7407 & 0.485 & 0.5641 & 0.762 \\ Group 5 & 0.425 & 0.741 & 0.488 & 0.721 & 0.804 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance of five methods on different Src groups.  
As shown in Figure 5(a), the accuracy of tour algorithm is 80.44%, which is significantly higher than the other four algorithms. Furthermore, we studied the impact on classification accuracy according to the change of Tartrain size. Parallel with the division method in Table 4, we ran the test with MCI1/NC3 (Tartrain) size at 15, 20, 25, and 30, respectively (see Lab2-Lab5). The accuracy of classification on five methods was listed in Figure 5(b)-e for the different Tartrain size. Figure 5(a)-e showed that the accuracy of proposed algorithm achieved the highest scores among all five methods. Meanwhile, we could see there was no significant difference in the TrAdaboost algorithm among all Src groups, which suggests that Src grouping has little impact on the accuracy of TrAdaboost algorithm. In comparison, the AD/NC1 group as Src appeared to have higher accuracy in the other four classic methods, but it obviously lacked stability. Based on the above results, we concluded that dividing the training data has no significant influence on the accuracy of transfer learning method. It also suggests that the results are more reliable with a bigger sample size. Consequently, the training data was no longer divided in the following experiments, and we just used AD/NC1 as Src data.  
When comparing Figure 5(a) with Figure 5(e), the accuracy of our algorithm fluctuated the most in Figure 5(b), and the details are listed in Table 6. Based on the various sizes of Tartrain data, the classification performance on AD/NC1 group were shown in Figure 5(e) and Table 7. The classification results showed that our algorithm had the highest accuracy among all five methods. Moreover, in Figure 5(e), we can see the classification accuracy in TrAdaboost algorithm increased as the sample size of Tartrain increased. The details are listed in Table 7.  
Figure 6: Comparison results.