## 3 Results and Discussion  
### Classification Comparison  
Contrast experiments were performed for the classification of four pathological subtypes of GGNs using FFDC and OFDC. To avoid large fluctuations in classification accuracy \begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Equation** \\ \hline True positive rate (TPR) or _recall_ & \(\text{TPR}=\text{TP}/(\text{TP}+\text{FN})\) \\ True negative rate (TNR) & \(\text{TNR}=\text{TN}/(\text{FP}+\text{FN})\) \\ False positive rate (FPR) & \(\text{FPR}=\text{FP}/(\text{FP}+\text{TN})\) \\ False negative rate (FNR) & \(\text{FNR}=\text{FN}/(\text{TP}+\text{FN})\) \\ _precision_ & \(\text{precision}=\text{TP}/(\text{TP}+\text{FP})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Typical assessment metrics. Figure 4: Schematic diagram of the GGN subtype classification based on FPDC. and to ensure that the training process can learn sufficient features, we retained ten cases of four subtypes in the dataset as the test set, and the rest were used to train and develop the classification model after implementing SMOTE. The sample distribution in SS is shown in Table 2.  
We input samples in \(SS\) into the subspace KNN classifier for pathological subtypes classification, \(k\) = 1, 2,..., 20. KNN begins with \(k\) nearest labeled neighbors of each sample to determine the label of any unknown sample. In general, with reference to the integer value of \(k\), the prediction output of classifier is determined on the basis of the majority vote cast by the neighbor class [36]. Namely, if any sample \(X_{C}\) is assigned to class \(C_{1}\), it must be the maximum probability of \(X_{C}\) belonging to class \(C_{1}\), expressed as  
\[KNN(X_{C})=\text{max}P(C_{1},X_{C}). \tag{12}\]  
where \(P(C_{1},\,X_{C})\) denotes the probability of \(X_{C}\) in class \(C_{1}\). In this paper, the ensemble method was set as the subspace to improve the classification accuracy of each independent classifier. KNN was applied to the pathological subtype classification as the learner. A total of three hyperparameters were included in the training process (i.e., the number of nearest labeled neighbors \(k\), the number of learners, and the subspace dimension). By combining grid search and cross validation, we avoid the situation where the selection of the model and parameter depends greatly on the partition method of the dataset. The selection interval of the nearest labeled neighbors \(k\) and the number of learner parameters was 1-10 and 1-100, respectively. The number of predictors to sample for each random subspace learner was specified as a positive integer in the interval 1,... \(p\), where \(p\) is the number of predictor variables. For FFDC and OFDC, \(p\) is 142 and 680, respectively, corresponding to the maximum feature dimension after feature selection. The five-fold cross validation was applied in the training process. The datasets were divided into five equal parts, using four folds as the training sets and the remaining for validation. The optimal combination of parameter values was selected by grid search with the aid of the five-fold cross validation. The confusion matrix results and the ROC curve for classification Are shown in Figure 5. Based on the confusion matrix, we calculated the corresponding evaluation indices. The experiments based on FFDC were: MavA was 72.5%, MavG was 72%, MFM was 0.75, and AUC value was 0.83. However, the comparative experiments based on OFDC were as follows: MavA was 67.5%, MavG was 66.2%, MFM was 0.68, and AUC value was 0.78. These quantitative evaluation metrics showed the same conclusion that the FFDC method yielded higher classification performance than the OFDC method in classifying four pathological subtypes of GGNs. This outcome proves that the FFDC method was effective. Additionally, we found that the classification performance of both methods for MIA was slightly poor. According to the analysis, MIA is the transitional period of pathological changes between pre-invasive and IA.