# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 3 Methods
### Selection of Methods and Evaluation Based on State-of-the-Art Metrics
In this proposal, the weights assigned to each integrity characteristic precision, reliability, completeness, and consistency reflect their relative importance in validation quality. Precision is given a higher weight (0.4) due to its critical role in the accuracy of predictions in classification systems, particularly in the validation of volatile data integrity, where errors can have financial consequences. Although precision is prioritized, the other characteristics are also integrated to ensure system stability. Additionally, the weights can be adjusted dynamically based on historical and test results, optimizing the balance in weighted voting without allowing any one characteristic to dominate excessively. Figure 4:0 Initial distribution of the weights assigned in the integrity assessments. The purpose of the voting method is to combine the contributions of all integrity characteristics to determine the final class. For each class \(y_{k}\), the score is calculated as the weighted sum of the integrity characteristics:  
Figure 4:1  
where \(W_{label}\) is the updated integrity characteristic \(label\) and \(V_{label,\ y_{k}}\) is the value of that characteristic for class \(y_{k}\). The class with the highest score will be selected as the final prediction. The final score of each class is calculated by summing the weighted contributions of all integrity characteristics. The class with the highest score will be the final prediction:  
Figure 4:2  
This score reflects the total quality of the data provided by the oracle, considering all the key integrity characteristics. Selecting the class with the highest score ensures the most reliable final prediction. Each model \(M_{i}\) contributing to the system has an integrity score \(S_{i}\). The prediction value of each model \(V(i)\) is multiplied by its integrity score to obtain its weighted contribution \(C(i)\) :  
Figure 4:3  
This step ensures that each model's contribution is weighted according to its quality and reliability, improving the accuracy of the voting process. Once the weighted contributions of all models are calculated, they are summed for each possible class. It generates an accumulated score for each class, as shown in the following expressions:  
Figure 4:4 Figure 4:5  
where \(1\) (\(\cdot\)) is an indicator is an indicator function that takes the value \(1\) if \(V(i)\) matches the corresponding class (0 or 1), and 0 otherwise. The class with the highest accumulated score is selected as the final prediction:  
Figure 4:6  
This process ensures that the final class selected has the highest support regarding the weighted contributions from all models. An example:  
Models: \(M1\), \(M2\), \(M3\), \(M4\)  
Integrity characteristics: _Prec_, _C_rec_, _C_con_, _C_cons_. Model predictions for a test instance (binary classification example):  
\(M1=1\)  
\(M2=1\)  
\(M3=0\)  
\(M4=1\)  
Initial integrity characteristics for each model:  
\(S_{1}=0.95\) (\(M1\))  
\(S_{2}=0.85\) (\(M2\))  
\(S_{3}=0.75\) (\(M3\))\(S_{4}=0.90\) (\(M4\))  Relevance of each characteristic: \(V_{Prec}=0.8\) \(V_{C\_rec}=0.9\) \(V_{C\_rec}=0.9\) \(V_{C\_com}=0.85\) \(V_{C\_cons}=0.75\) Step 1: Calculation of Feature Relevance  The relevance of each feature is calculated and understood as the relative impact of each integrity feature on data quality. The relevance values are normalized to ensure that the sum of the relevancies is 1. Example of relevance calculation (\(R_{label}\)) for each feature:  
Figure 4:7  
Figure 4:8  
Figure 4:9  
Figure 5:0  
Step 2: Assigning Weights to Integrity Characteristics  
Weights are assigned to the integrity characteristics based on the calculated relevances. The weights are the proportion of the significance of each feature to the total sum of relevance.