## 2 Literature Review
They first applied a spatial filter to WBMs to extract relevant features for defective pattern identification. Then, they used hybrid clustering to define cluster types based on the extracted features. To classify individual WBMs into one of these identified clusters (e.g., linear type, elliptical type, and ring type), various classification methods such as classification module, estimation module, Gaussian EM algorithm, and spherical shell algorithm, were employed. [PERSON] and [PERSON] [22] proposed a hybrid SOM-SVM method to recognize and classify WBM defect patterns. In their framework, SOM was trained to discover representative WBM defect patterns, whose clustering membership became the target class of the SVM classifier. Experimental results showed that the proposed method can correctly classify the discovered defect patterns with more than 90% accuracy. [PERSON] et al. [4] attempted to extract significant features using polar Fourier transform and rotational moment invariants, both of which are commonly used in signal processing. They trained classifiers to classify six predefined WBM defect patterns, i.e., bulls-eye, blob, hat, ring, line, and edge, based on the extracted features. A decision tree, [PERSON]'s classifier, bagging decision tree, and boosted logistic regression were employed as classification algorithms. Although this method showed good performance for simulated WBM datasets (68.33% accuracy), its performance deteriorated when applied to a real WBM dataset (52.58% accuracy). Another notable limitation is that the WBM defect patterns were predefined and were not changed over time. Since currently non-existent defective patterns can be observed during the actual manufacturing process, this method is not flexible enough to adapt to unexpected changes in manufacturing environment. [PERSON] et al. [23] proposed a defect-type detection method based on similarity searching to address problems of not only inflexibility caused by predefined defect patterns but also the difficulty of capturing complex defect patterns when more than two defect patterns occur simultaneously. To do so, they first performed a morphological sample generation process to reproduce various pattern derivations (shift, erosion, dilation, opening, and closing) from the original defect patterns. Then, they used two types of training datasets: predefined defect patterns determined by experienced engineers and dissimilar samples determined by the 1-SVM. The SVM classifiers were trained to classify the five defect patterns. The experimental results showed that the proposed method achieved 95% catching rate with only 5% false-alarm rate. However, this model can only be applied to predefined defect patterns. [PERSON] et al. [24] and [PERSON] et al. [25] employed ANN-based classifiers called simplified sub-spaced regression network and randomized general regression network to improve the WBM defect pattern classification performance. Their first proposed model [24] yielded superior and robust performance, in addition to improved computational efficiency compared with the benchmarked six methods. In the following study [25], they proposed a classification model with a bagging ensemble scheme that guaranteed relatively stable prediction accuracy and low variance. The proposed method achieved approximately 99.79% classification accuracy. [PERSON] et al. [26] proposed a two-phase method that first discovered defect patterns and then classified WBMs into one of these patterns based on different image feature extraction methods. They employed random-based features, geometry-based features, and Hough transformation features for feature extraction. Then, the SVM classifier was used to classify the WBM. Their proposed model yielded 94.63% classification accuracy in nine-class (eight defect classes + no class) classification. The aforementioned studies mainly focused on discovering representative defect patterns that appear on WBMs and classifying individual WBMs into one of these discovered classes.