## 2 Materials and Methods
### Machine Learning Classifiers
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Feature Name**} & \multicolumn{4}{c}{**Before Normalization**} & \multicolumn{4}{c}{**After Normalization**} & \multirow{2}{*}{**Coef Diff**} \\ \cline{3-3} \cline{5-8}  & & **Coef** & \(p\)**-Value** & \
\begin{tabular}{c} **Predictable** \\ **(\(p\) \textless{} 0.05)** \\ \end{tabular} & **Coef** & \(p\)**-Value** &
\begin{tabular}{c} **Predictable** \\ **(\(p\) \textless{} 0.05)** \\ \end{tabular} & **Coef Diff** \\ \hline
1 &
\begin{tabular}{c} Wavelet Entropy Rate (ER) \\ \end{tabular} & \(0.1880\) & \(0.0011\) & \(1\) & \(0.1229\) & \(0.0340\) & \(1\) & \(-0.0651\) \\ \hline
2 &
\begin{tabular}{c} **Average Useful** \\ \end{tabular} & \(0.1880\) & \(0.1229\) & \(0.0651\) \\ \hline
2 &
\begin{tabular}{c} Wavelet Bandwidth (WB) \\ \end{tabular} & \(-0.1565\) & \(0.0068\) & \(1\) & \(-0.0889\) & \(0.1256\) & \(0\) & \(0.0000\) \\ \hline \multicolumn{3}{c}{**Average All**} & \multicolumn{2}{c}{\(0.1723\)} & \multicolumn{4}{c}{\(0.1059\)} & \multicolumn{4}{c}{\(0.0664\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Wavelet-domain features ranked by correlation coefficient. intoxication detection [66; 67; 68; 69; 70; 71]. We applied classification to features with a \(p\)-value of \(<\)0.05 Random forest is an ensemble learning method that constructs multiple decision trees for classification, regression, and other tasks. It makes predictions by averaging outputs or selecting the most frequently predicted class, mitigating the tendency of individual decision trees to overfit [72; 73]. J48 is a decision tree classifier that generates pruned or unpruned C4.5 decision trees. Developed by [PERSON] [74], C4.5 is a classification algorithm that constructs decision trees based on information entropy [75] as shown in Equation (5), in order to maximize the information gain at each step. It is widely regarded as a statistical classifier [76]. \[H(S)=-\sum_{i=1}^{n}P_{i}\log_{2}P_{i} \tag{5}\]  
JRip, based on the repeated incremental pruning to produce error reduction algorithm by [PERSON] [77], is an optimized version of incremental reduced error pruning. It splits training data into a growing set and a pruning set, first forming an initial rule set. The rule set is then iteratively pruned using operators that minimize error in the pruning set. The process stops when further pruning increases the error. Naive Bayes classifiers are probabilistic models based on [PERSON] theorem, shown in Equation (6), with independence assumptions between features. In WEKA, the naive Bayes classifier applies the \"maximum a posteriori\" (MAP) rule shown in (7) to maximize the prior probability distribution, as shown in Equation (3). This approach makes naive Bayes highly scalable, with parameters scaling linearly with the number of features [75; 78]. \[P(\theta|x)=\frac{P(x|\theta)P(\theta)}{P(x)} \tag{6}\]  
\[\arg\max_{\theta}\int P(x|\theta)P(\theta)=\arg\max_{\theta}\int P(x) \tag{7}\]  
SVM, based on [PERSON]'s statistical learning theory, solves binary classification problems by finding an optimal separating hyperplane (OSH) that maximizes the margin between classes. The points on the margin's edge, called support vectors, define the OSH. In WEKA, SVM is implemented using the sequential minimal optimization (SMO) algorithm [79; 80]. A decision table elegantly maps conditions to corresponding actions, similar to flowcharts and if-then-else statements. Each decision represents a variable or predicate with possible values, while actions define operations to perform based on condition alternatives.