## 1 Introduction
### Self-Healing Networks
#### 1.5.3 Methods Based on Data Mining  
* **Supervised Methods**  
Due to the issues that exist in this domain, and have already been referred to, the present methods in the area of supervised methods primarily investigate in a restricted way. The techniques employed are often limited to Bayesian networks. The most effective techniques in this area are discussed in the following sections. There has been much interest in using Bayesian networks to represent network events and communicate between faults and their causes [21]. The primary distinction between these approaches depends on the structure used for identifying the cause of the fault and the various fault indicators and reasons that the suggested solution can look into. A model for locating defects in cellular networks using the Naive Bayesian classification method was introduced in [21]. The fault causes are treated as classes, and the values of various indicators are treated as features. The introduced model, providing the knowledge base used for classification, is divided into two primary categories: quantitative and qualitative. The quantitative section comprises the links between these components, and the qualitative part includes the model's components. This article demonstrated how the average beta density function parameters could be used to thoroughly specify the relationships between the model's parts. By discretizing the index values and using the Spartan Bayesian classification approach, [22] was able to identify the fault source. The entropy minimization discretization (EMD) method was applied to boost the effectiveness of the proposed model. Utilizing this technique resulted in the best possible selection of useful indications from the input parameters. The study in [23] aimed to improve a network's accuracy by considering the continuous nature of the index values. Giving them discrete values decreased the precision of the results produced. As a result, the Smooth Bayesian network was employed with varying state uncertainty. On the other hand, as these parameter values were considered, more data was required to calculate the parameter values. The findings of the applied method,which used Bayesian law, are displayed in [24], utilizing a dynamic simulator system on the UMTS network. The architecture for employing these networks' troubleshooting tools was provided. It is always necessary to strike a balance between the precision and complexity of a model. The DCR index calculated the number of disconnected calls, which was the fault detection criterion in [20] (thoroughly explained in the second section). The system experienced a fault if the value of this index exceeded the threshold, and the causes of the fault were determined by looking at the simplified Bayesian network and utilizing the Independence of causal impact (ICI) approach between the indicators and the faults' causes. The simplified Bayesian technique was dealt with using the ICI method, and each fault source was employed as an independent node with two true or false states in the modeling. The outcomes of the simulations demonstrated that the effectiveness of these two approaches was equivalent but that the ICI approach was more complex. As a result, the simplified Bayesian model was given precedence. These techniques employed reverse engineering to create faults under particular circumstances. In other words, the methodologies utilized in the paper attempted to model the fault using the value of the key performance indicators when the fault occurred, even though the source of the issue was previously known. The absence of a database of classified instances that can be utilized to estimate the parameters of the suggested model is one of the issues in this subject. A technique for developing a statistical model using expert knowledge was provided in [25]. In this method, knowledge acquisition was accomplished in two stages: first, with the assistance of experts, knowledge was gathered, and then models were built.