# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 4 Machine Learning Applied to Imputation and Data Augmentation to Increase Classification Accuracy
### Machine Learning Applied to Surface Roughness Prediction
#### 4.3.2 CART, n-Dimensionality and Augmentation  
CART was used as a second machine learning technique to get a different perspective of learning capabilities when compared with neural networks. Table 3 gives similar output measures to Table 2 to be able to give a good comparison. That said, classification trees were more suited for this type of data than NNs, as the 'curse of data dimensionality' has less of an affect when compared with NNs. The distance error measure, however, increased as augmentation percent levels increased--also, the unseen test case accuracy \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & **Data Size** & \multirow{2}{*}{**SSE**} & **Distance** & **Time to** &
\begin{tabular}{c} **Unseen** \\ **Test Case** \\ **Accuracy** \\ \end{tabular} & \multirow{2}{*}{**Iterations**} & **\% Aug \({}^{*}\)** \\  & **(Columns)** & & & & **Accuracy** & **Increase** \\ \hline mp (Tx) & 98 (2) & 7.31 \(\times\) 10\({}^{16}\) & \(-\)0.02633 & 0:04:48 & 20/20 & 20,000 & 0 \\ \hline mp (TXLx) & 98 (3) & 6.65 \(\times\) 10\({}^{15}\) & \(-\)0.02454 & 0:04:46 & 19/20 & 20,000 & 0 \\ \hline mp (TxLx) \& Param & 98 (6) & 4.24 \(\times\) 10\({}^{14}\) & \(-\)0.77117 & 0:05:11 & 17/20 & 20,000 & 0 \\ \hline mp (TxLx) \& Param \& Augmented & 108 (6) & 6.09 \(\times\) 10\({}^{11}\) & \(-\)0.33589 & 0:10:26 & 18/20 & 20,000 & 10 \\ \hline mp (TxLx) \& Param \& Augmented & 118 (6) & 1.00 \(\times\) 10\({}^{10}\) & \(-\)0.28471 & 0:10:30 & 17/20 & 20,000 & 20 \\ \hline mp (TxLx) \& Param \& Augmented & 158 (6) & 1.10 \(\times\) 10\({}^{9}\) & \(-\)2.54116 & 0:12:10 & 13/20 & 20,000 & 60 \\ \hline mp (TxLx) \& Param \& Augmented & 205 (6) & 6.03 \(\times\) 10\({}^{9}\) & \(-\)5.31443 & 0:12:46 & 11/20 & 20,000 & 100 \\ \hline mp (TxLx) \& Param \& Augmented & 158 (6) & 2.80 \(\times\) 10\({}^{12}\) & \(-\)2.32811 & 0:11:39 & 17/20 & 20,000 & 60 \({}^{**}\) \\ \hline \hline \end{tabular}  
* Key: \({}^{*}\) Aug = Augmentation; \({}^{**}\) Other half of data; Tx: Transversal measurement; Lx: Longitudinal measurement.  
\end{table} 
Table 2: Neural Network results for normal data and different levels of dimensionality and augmentation. started to decrease. As the results are somewhat similar to NNs, this second machine learning technique further reinforces the findings of the increases in dimensionality and augmentation in terms of accuracy and learning capability. The classification tree for the Transversal or Longitudinal sensor orientation BN (mp) values promote much smaller trees due to the amount of information present. With extra parameters provided by the focused Transversal BN values, the classification tree increases and caters for more complex demarcation when differentiating more data with a higher confidence of the phenomena under measurement. With augmentation of the data, the rules and complexity increase, however the demarcation of boundaries within the measurement are also increased. The augmentation forces X1 and X2 variables to segregate the different surface quality outputs, where only X1 is used for sole transversal measurement data information. Having extra parameters provides more data dimensions for segregating the information of interest. A divide-and-conquer approach was not necessary to classify any overlapping data groups as the unforeseen combined data were also predicted with a 100% accuracy. From these results, it can be concluded that surface roughness can be correlated to different material samples, which could be used to add or subtract such effects from an overall NDT model or assist in furthering the understanding of magnetic effects with magnetic sensing technologies. All of the ML metrics were considered to be connected where they all gave a similar pattern to the learning capability.