# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 5 Comparison between ML-Based Methods for AE
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Kernel** & **Accuracy (\%)** & **Others** \\ \hline Fine tree & - & 90.2 & Num. of splits = 100 \\ \hline Medium tree & - & 75.8 & Num. of splits = 30 \\ \hline Linear discriminant & - & 73.9 & Full covariance structure \\ \hline Gaussian naïve Bayes & - & 66.8 & Gaussian distribution \\ \hline Support vector machine & Quadratic & 97.6 & Automatic kernel scale \\ \hline Support vector machine & Cubic & 97.9 & Automatic kernel scale \\ \hline Support vector machine & Gaussian & 92.3 & Kernel scale = 7.9 \\ \hline KNN & - & 98.3 & Euclidian distance metric \\ \hline KNN & - & 91.5 & Cosine distance metric \\ \hline Boosted trees & - & 95.1 & AdaBoost ensemble \\ \hline Bagged trees & - & 97.5 & Bag ensemble \\ \hline Subspace KNN & - & 98.3 & Subspace ensemble \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of different ML-based models applied. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Type** & **Data** \\ \hline Piezoelectric transducer & Sensitivity & 69 V/(m/s) \\ Piezoelectric transducer & Resonant frequency & 150 kHz \\ Piezoelectric transducer & Directionality & \(\pm\)1.5 dB \\ DAQ-NI-6110 & Channels & 4 \\ DAQ-NI-6110 & Resolution & 12 bit \\ DAQ-NI-6110 & Sampling frequency & 5 Msample/s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics of instrumentation used for data collection. * Decision tree: The decision tree is a graph characterized by nodes and edges. The highest no-do is defined as the root node; each intermediate node represents a test carried out on a certain attribute, and the arcs represent the result of the test. The attributes against which the test is performed in each intermediate node are called splitting attributes as they divide the data into subgroups. The goal of the algorithm is to carry out various tests on the values of the attributes starting from the root node, so that each record, based on the result obtained in each test, travels a more or less long path between the various intermediate nodes until arriving at a leaf node that defines its class to which it belongs. The decision tree has a structure like a flowchart: it consists of internal nodes (the first at the top is called the root node, and the following ones are called child nodes), which represent the subsets of the initial dataset divided according to the attributes; branches, which represent the decision-making rules according to which the division takes place; and leaf nodes, which represent the final subsets into which the initial database was split. * Linear discriminant: The purpose of the analysis is to find one or more linear combinations of parameters that allow optimal discrimination between the various groups. In this way, an observation can be attributed to a given group based on the measurements. The methods of linear discriminant analysis can be justified by assuming that the distribution is, within each group, normal with a common variance-covariance matrix and based on semiempirical criteria of separation between groups, without hypothesis, and distributional on variables. The goal is to project the n-dimensional space of the features of the input data into a smaller subspace, removing the redundant and dependent features. The classification is based on three steps: calculate the separability between different classes (for example, the distance between the means of different classes), calculate the separability between the elements of the same class, and build a space smaller than the starting one in which the separation between the different classes is maximized and the separation between elements of the same class is minimized. * Gaussian naive Bayes: Using the Bayes theorem, the algorithm allows you to assign a label to each group of text to facilitate their classification. This is what spam filtering, a popular application of the naive Bayes algorithm, does. Basically, it is among the most popular learning methods that group analyzed data based on their similarity.