## 4 Conclusions  
The development of efficient and reliable methodologies represents an extremely important task for researchers and developers of diagnosis systems; in order to contribute to the solution of this task, DM techniques have been widely used. To offer the reader an overview of DM techniques used in the detection of faults and diagnosis of electrical equipment and systems in recent years, this paper provides a general review that can facilitate informed decision-making for specific applications. All the details and results obtained by the authors cited here can be consulted directly from the bibliography of each research. Although certain techniques have been constantly used for specific applications, e.g., SVMs in transformers, the selection of an appropriate DM technique for either classification or regression will depend on many factors, e.g., monitoring technology, features of data, and knowledge about the in-test system operation. However, it is important to take into account that the more complex and robust the systems, the greater the amount and variety of data produced, and the more difficult the detection of faults and the diagnosis. Additionally, the researcher has to be informed about the features of specific DM algorithms so that, through its implementation, the information contained in the acquired data can be exploited. It is extremely difficult for a single technique to detect the full range of faults of a system in a 100% reliable way. Each method has its own strengths and weaknesses. Outputs from various diagnostic methods must be aggregated into an overall evaluation system; thus, instead of using one diagnostic method, intelligent hybrid methods that combine the strengths of each method can be developed. In the literature, there are many articles using hybrid techniques in order to increase the percentage of efficiency, accuracy, reliability, and speed of their models. On the one hand, different signal processing techniques have been used for pre-processing of data. This pre-processing allows \begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Methods** & **Usage** \\ \hline \hline \multirow{6}{*}{2018} & Heterogeneous-target robust mixture regression (HERMIT) [(70)] & Handling of heterogeneous data \\ \cline{2-3}  & Markov chain sampling [(118)] & Classification of data in presence of label noise \\ \cline{2-3}  & Prototype-based classification [(119)] & Learning and prediction based on the selection of handfuls of class data \\ \cline{2-3}  & Kernel mixture model [(120)] & Probability density estimation in Bayesian classifiers \\ \cline{2-3}  & Linear regression [(126)] & Uplift modeling \\ \cline{2-3}  & Random forest, XGBoost, and LSTM [(128)] & Analysis of temporal stability and accuracy for binary classification \\ \cline{2-3}  & Convolutional NN [(130)] & Intelligent fault diagnosis when the data at training and testing time does not come from the same distribution \\ \cline{2-3}  & Growing Neural Gas Algorithm [(131)] & Adaptive algorithm got evolving data streams \\ \hline \hline \multirow{3}{*}{2019} & Least squares support vector machine [(132)] & Imbalance of data \\ \cline{2-3}  & Logistic regression and Kalman filter [(133)] & Estimation error reduction in online sequential extreme learning machine systems \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._highlighting and extracting information from raw data. Typical operations are denoising, frequency or mode decomposition, and space transformation, where the WT- and EMD-based methods have demonstrated promising results. On the other hand, the combination of different DT algorithms has been also explored in order to take advantage of their individual benefits. [PERSON] et al. (2014) present an important analysis on the 10 most influential DM algorithms in the research community, being C4.5, CART, PageRank, k-Means, kNN, Apriori, AdaBoost, Expectation-Maximization, NB, and SVM. It should be noted that these algorithms cover statistical learning, classification, clustering, link mining, and association analysis.