# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 5 Classifier
### Unsupervised Classifier  
The self-organizing map (SOM) is an important type of neural network based on unsupervised learning methods that was first put forward in 1981. Since then, with the rapid development of neural networks in the mid to late 1980s, self-organizing map theory and its applications have also made considerable progress. The self-organizing map network conducts classification by finding the optimal set of reference vectors. Compared with the traditional pattern clustering method, the clustering center can be mapped to a surface or a plane while keeping the topology unchanged. Hence, the problem of discriminating unknown cluster centers can be solved by using self-organizing maps. For example, in Kang [42], the authors researched an adaptive classification technique based on a combination of supervised learning neural network with error back-propagation (NN-BP) and unsupervised learning (SOM).  
Table 7 shows the performance comparison of classification methods for steel defects recognition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Classifier** & **Strengths** & **Weaknesses** \\ \hline \multirow{6}{*}{KNN} & The algorithm is simple, clear, and easy to achieve. & Time consuming (time complexity is O(n)). & Time consuming (time complexity is O(n)). \\ \cline{3-4}  & & Not suitable for the unbalanced distribution of samples. \\ \hline \multirow{6}{*}{ANN} & Suniable for nonlinear separable problems. & Too many layers (i.e., more than eight layers) make it prone to overfitting. \\ \cline{3-4}  & & High classification accuracy. & \\ \cline{3-4}  & & Suitable for small sample sets. & Difficult to find the kernel function. \\ \cline{3-4}  & & Good generalization performance and robustness. & \\ \cline{3-4}  & & Suitable for nonlinear problems. & Solving quadratic programming of functions requires a lot of storage space. \\ \cline{3-4}  & & Suitable for high-dimensional situations. & \\ \hline \multirow{6}{*}{LF} & Suniable for multi-class classification. & Difficult to determine the coefficients of functions. \\ \cline{3-4}  & & Accurate and efficient. & \\ \cline{3-4}  & & Suitable for expressing qualitative knowledge and experience in cases of unclear boundaries. & Poor classification accuracy (60\%–85\%). \\ \cline{3-4}  & & Simple structure. & Input of heterogeneous samples will prevent convergence of weight vectors. \\ \cline{3-4}  & & Adaptive ability. & Information of each dimension of the input sample is not fully utilized. \\ \hline \multirow{6}{*}{Unsupervised classifier} & \multirow{2}{*}{SOM} & Suitable for identifying unknown cluster centers. & \multirow{2}{*}{“Dead nodes” can appear when the number of neuron nodes is more than the number of categories.} \\ \cline{3-4}  & & Fault tolerant function. & \\ \cline{3-4}  & & Self-associative function. & \\ \hline \hline \end{tabular} Note: KNN: K-nearest neighbor, ANN: Artificial neural network, SVM: Support vector machine, DF: Discriminant function, FL: Fuzzy logic, LVQ: Learning vector quantizer, SOM: Self-organizing map.  
\end{table}
Table 7: Strengths and weaknesses of classifiers for steel defect recognition.