Machine learning has been used to analyze the ECT signal for detection and classification of the hidden corrosion in the multilayers structure. Principal component analysis (PCA) was used to detect notches at the fastener hole in the P-ECT probe [10]. The P-ECT probe has eight pick-up coils around an exciting coil, which makes it possible to detect all the positions of the notches around the fastener hole. In another study [17], surface and subsurface defects were classified by a combination of the PCA features, spectral characteristics features, peak value, and peak time features. PCA was applied to extract features of the PECT signal of the defects at the upper layer and deeper layer, then a random forest (RF) classifier was used to classify the defects according to their localization [18]. A convolutional neural network (CNN) has been proposed to classify defects in different layers and simultaneously evaluate defect depth [19]. Additionally, a support vector machine (SVM) method has been used to classify the internal states of the multilayer structure with the combination of lift-off features and PCA features [20]. With the machine learning techniques, defects in the multilayer structure could be accurately classified. However, the ECT probes contain a sensing element [17; 18; 19; 20] or a few sensing elements (e.g., eight pick-up coils [16]), which make the inspection operation complicated and time-consuming while scanning. In addition, the use of pick-up coils limits the spatial resolution of the sensor probe due to the size of the coil. Magnetic sensor arrays were developed [21; 22; 23; 24], which have multiple magnetic sensor elements arrayed in a high spatial resolution. The use of multiple magnetic sensors (e.g., Hall sensors [21; 22] and GMR sensors [23; 24]) in the ECT probes helps to measure the higher quality of the corrosion in the multilayer structures. However, the electromagnetic field induced by the rivet and fastener hole was observed to be very large compared to the field induced by corrosion; thus, it is difficult to detect corrosion [21; 22]. In addition,the special design of the magnetic sensor probe uses only a single large magnetic source for the sensor array; thus, the measured magnetic field distribution around the rivet and corrosion has a strong correlation. Two-layer specimens similar to the skin of the aircraft intake were fabricated and tested with and without artificial corrosion. The hidden corrosion in the second layer at different locations around the fastener holes was detected and classified according to its location. The previous study used handcraft features, \"Sum of area\" (SoA) [22], for the detection of the corrosion. Four areas around the rivet were selected, summed, and compared with thresholds. This method could detect the smallest corrosion with a volume of 46.35 mm\({}^{3}\) with 90% probability of detection (POD) and 95% confidence. However, the SoA features were not considered the correlation of the signals at the corrosion area and rivet site. Thus, in this paper, data-driven learning features will be extracted by principal component analysis (PCA) on the correlation of all the sensor signals. Then, machine learning techniques will be applied to the learning features to detect and classify the hidden corrosion in the multilayer structure. The machine-learning models will be trained on different ranges of corrosion volume and a different number of corrosion samples to find an effective training dataset with fewer data (e.g., 20% on a small corrosion volume from 15.0 to 25.4 mm\({}^{3}\)). The results could be helpful for further design of experiments, especially in the electromagnetic testing where the data sample is limited. The improvement of POD will be also obtained and compared with the previous study.