# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 4 Case Study
### Supervised Learning Analysis Based on Proposed Method
#### 4.4.1 Classification Results Analysis with Few-Shot Samples  
In the primary learning process, 189 labeled spare parts from the auxiliary system are selected: four from class A, 26 from class B, 43 from class C, and 116 from class D. The dataset is divided into training and testing sets. All the spare parts data are transferred into colored hierarchical structure and saved as images. Subsequently, all the images are processed in the modified AlexNet model as discussed in Section 3.2. The modified AlexNet structure has five convolution layers and three fully connected layers. Every image is 227 \(\times\) 227 in size; the ReLU activations ensure that no outputs after the convolution or fully connected layers have overfitting problems. Once the output is calculated and output by the last fully connected layer, a Softmax layer determines the final category of the image. The final classification results in this case split the images into four different classes: A, B, C, and D.  
For the 189 images in this step, the first random 100 images are trained using the modified AlexNet, as shown in Figure 9. Some of the classification results are shown in Figure 8. It is indicated that the four results are D, B, C, D respectively. It is obvious that the listed four spare parts images are all classified with an accuracy of 100%. Additionally, for the overall training result, the average accuracy of the modified model reaches 99.8%, which can fully satisfy the practical demand. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Clients** & \(\mathbf{N_{p}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b}}\) & \(\mathbf{X_{b0}}\) & \(\mathbf{X_{b1}}\) & \(\mathbf{X_{b2}}\) & \(\mathbf{X_{b3}}\) & \(\mathbf{X_{b4}}\) & \(\mathbf{v}\) \\ \hline \hline \multirow{3}{*}{
\begin{tabular}{c} update \\ gender \\ color \\ \end{tabular} } & 1 & 1304.76 & 6 & 2 & 3 & 6 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2083.56 & 1304.76 & 9013.22 \\ \cline{1-1}  & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The original data information of spare part No. 144. Figure 9: Partial training results of initial dataset. (**a,d**) indicate these two spare parts are 100% classified into class D; (**b**) shows this spare part is 100% classified into class B; (**c**) shows this spare part is 100% classified into class C.  
Then, the constructed model is also used to test the remaining 89 images, and the commonly used indicator recall ratio \(r\) and precision ratio \(p\) are adopted to evaluate its performance:  
\[r=\frac{TP}{TP+FN} \tag{8}\]  
\[p=\frac{TP}{TP+FP} \tag{9}\]  
where \(TP\) is a true positive classified correctly as positive, \(FN\) is a false negative misclassified as false, and \(FP\) is a false positive misclassified as positive. The test accuracy of each category is determined here as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{10}\]  
where \(TN\) is a true negative classified correctly as negative. The classified results for \(TP\), \(FP\), \(TN\), and \(FN\) are shown in Table 5, and the results of \(r\) and \(p\) for the testing are shown in Table 6.  
For practical application in the spare parts classification, as shown in Table 5, there is one sample in class A that is classified as false positive. It is the true spare part No. 23 that is easily classified to be A because both its unit price and annual inventory cost are high. However, in the actual management, this item is classified into B class since it is in high monitoring and the supply reliability is high. As shown in Table 5, the overall accuracy for B, C and D is 86.7%, 87.5% and 97.9%, respectively. However, the accuracy for class A is merely 50%, since there are only two samples in the testing set. However, when the results are analyzed with precision and recall, it indicates the modified AlexNet structure appears to perform well. Especially for class A, the recall rate is 100%, although the precision ratio is only 50%.