## 3 Proposed Method
### False Positives Feedback Mechanism  
In order to refine the interpretability of the VAEGA model results, we subclassify the outliers according to the set of anomaly subspaces searched by the GA layer (see Algorithm 1). The structure diagram of this mechanism is shown in Figure 9.  
Figure 8. Chromosomes after uniform crossover.  
```
0: Outlier dataset \(O=\{o_{1},o_{2},...,o_{n}\}\);
0: Abnormal subspace set \(AS=\{(as)_{1},(as)_{2},...,(as)_{n}\}\);
1: Initialize the population \(Gen\), the number of individuals is \(N_{Gen}\), and set the crossover probability \(P_{c}\) and mutation probability \(P_{m}\);
2:for\(i=1\) to \(n\)do
3:Epoch = 1
4:while\(Epoch<Max_{Epoch}\)do
5: Calculate the fitness function for each chromosome
6:if\(SOD_{top}\) satisfies the optimal solution then
7:\(as_{i}\) = individual \(v_{-}(SOD_{top})\) decoded into abnormal subspace representation
8:else
9: Calculate the probability of selection of a chromosome
10: Use the best retention to select the value of \(p_{v_{i}}\) the top \(N_{Best}\) chromosomes into the next generation
11: Select \(N_{k}\) chromosomes using no-replacement remainder selection rule among the remaining chromosomes
12:for\(j=1\) to \((N_{Gen}-N_{Best}-N_{k})\)do
13: Randomly select two parent chromosomes
14: Perform gene crossover according to \(P_{c}\) and uniform crossover rules to form two new chromosomes and join the cenozoic population
15:endfor
16: Perform mutation operation on random m chromosomes in the population according to \(P_{m}\)
17:Epoch = Epoch+1
18:endwhile
19:endfor
20:return Abnormal subspace set \(AS\) and weight values
```  
**Algorithm 1** Search Subspace Process  
Figure 9: Outlier sub-classification and false positive feedback process based on abnormal subspaces.  
The first step is to match candidate outliers' abnormal subspace features with those of existing outlier classes. A point will be assigned to the outlier class if it has the same abnormal features in its subspace. Otherwise, a new outlier subclass will be generated. This step will continue until all outliers are classified. We merge subclasses that contain only one outlier into a special class.  
In the second step, we focus on examining the special classes based on the subclassification results above. The points in the abnormal subspace in which each feature component is within its normal range are screened out, that is, those points whose abnormal feature values are within \(\pm 3\sigma\) of the corresponding feature data distribution. The selected points are deemed as misjudgments of the model and form a set of false positive points. After that, we use the set of false positive points to optimize the parameters of the VAE outlier detection module in order to reduce the false positive rate of the VAEGA model.  
In the final step, special filtered classes and other subclasses will be used to analyze the causes of anomalies. Often, outliers that occur in a special class can be more informative since they often result from the properties of the data themselves. People may be able to avoid substantial losses by paying attention to special outliers and taking action to remedy them as soon as possible.