## 2 Related Work and Background
### ID3 Decision Tree Algorithm  
The ID3 (Iterative Dichotomiser 3) algorithm, proposed by [PERSON], is a widely used supervised learning approach for classification and decision-making tasks [26]. The algorithm constructs decision trees using a top-down, greedy strategy based on information entropy and information gain. It recursively partitions the dataset by selecting attributes that maximize information gain, thereby effectively minimizing decision tree complexity and enabling efficient classification. Two key metrics underpin the ID3 algorithm: information entropy and information gain. Information entropy quantifies uncertainty in a dataset and is mathematically defined as follows:  
\[Entropy\;(D)=-\sum_{i=1}^{m}p_{i}\times\log_{2}p_{i} \tag{1}\]  
where \(D\) represents the dataset, \(m\) indicates the number of distinct classes, and \(p_{i}\) denotes the probability of class \(i\) within dataset \(D\).  
Information gain measures entropy reduction following dataset partitioning based on an attribute \(A\), expressed as  
\[\text{Gain}(D,A)=\text{Entropy}(D)-\sum_{v\in\text{Values}(A)}\frac{|D_{v}|}{|D| }\times\text{Entropy}(D_{v}) \tag{2}\]  
Here, \(Values(A)\) is the set of all possible values for attribute \(A\), and \(D_{v}\) denotes the subset of dataset \(D\) where attribute \(A\) equals value \(v\).  
The ID3 algorithm exhibits several advantages, such as simplicity, interpretability, and ease of implementation, making it widely applicable in practical classification scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Algorithm Name** & **Main Improvement** & **Advantages** & **Disadvantages** \\ \hline [PERSON] et al. [18] & Node Role Division & Division of nodes into & Clear role assignment & Complex role \\ PBFT & specific roles & and improved & management \\ [PERSON] et al. [19] & Grouping and Credit & Node grouping and & Efficient consensus and & Difficulty in accurate \\ PBFT & PBFT & credit grading & reduced overhead & initial grouping \\ [PERSON] et al. [20] & Trust-based Leader & Leader selection based & centralization risk, & Dependence on trust \\ Selection PBFT & on node trust & enhanced fault & evaluation accuracy \\ [PERSON] et al. [21] & PBFT for Microgrid & Optimized PBFT & High efficiency, secure & Limited to energy \\  & & electricity transactions & energy transactions & transaction scenarios \\ [PERSON] et al. [22] & Large-Scale & Improved scalability & Better scalability, & Higher complexity in \\  & Consortium PBFT & for large-scale & reduced latency & node management \\ [PERSON] et al. [23] & Reputation-Voting & Reputation-based & Enhanced fairness and & Complex reputation \\  & PBFT & before voting mechanism & robustness & voting processes \\ [PERSON] et al. [24] & Vague Sets PBFT & Integration of vague & Improved robustness & Increased \\  & & sets theory & under uncertainty & computational \\  & & Reputation and & Efficient consensus, & complexity \\ [PERSON] et al. [25] & Proof of Power & power-based node & reduced latency, fair & Complexity in \\  & Reputation (PPoR) & selection for V2V & node selection & reputation evaluation \\  & energy trading & mechanism & & \\ \hline \end{tabular}
\end{table}
Table 1: Analysis of PBFT algorithms and their improvements.  
However, it also presents limitations, including susceptibility to overfitting, especially with limited datasets having numerous attributes; necessity for discretization of continuous data; and sensitivity to noisy data, potentially reducing classification accuracy.  
Due to its interpretability and efficiency, the ID3 algorithm is increasingly integrated into blockchain consensus frameworks. Particularly in consortium blockchain systems, ID3 has been effectively utilized for dynamic node reputation assessment and precise node classification, contributing significantly to enhancing consensus reliability and automation [27].