## 3 Recent Methods for General Applications
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Data Mining Techniques** & **Other Techniques** & **Application** \\ \hline \multirow{2}{*}{\(\blacksquare\)} & CART and clustering algorithm [111] & - & \(\blacksquare\) & Ion implanter \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Auto-associative kernel regression, & & \(\blacksquare\) & Reactor Coolant Pump of a typical Pressurized Water Reactor \\  & correlation analysis, genetic algorithm, and & - & \(\blacksquare\) & \\  & probability ratio test [112] & - & \(\blacksquare\) & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & NN [113] & Optimal zoom search & \(\blacksquare\) & High-pressure feedwater heater system \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Decision tree [114] & DWT & \(\blacksquare\) & Face milling tool \\ \hline \multirow{2}{*}{\(\blacksquare\)} & NN and subtractive clustering analysis & - & \(\blacksquare\) & HVAC systems \\ \cline{1-1}  & [115] & - & \(\blacksquare\) & HVAC systems \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._types of objectives simultaneously, imposing structural constraints on each component of the mixture and adopting robustness strategies. On the other hand, [PERSON] and [PERSON] [127] describe a scalable end-to-end tree boosting system (XGBoost) and propose a new algorithm based on data dispersion providing information on cache access patterns, data compression, and fragmentation to construct a scalable tree boosting system. They claim that XGBoost is widely used by data scientists to achieve cutting-edge results in many machine learning challenges. [PERSON] et al. [128] evaluate the temporal stability and prediction accuracy of different existing predictive process monitoring methods, finding that the methods based on the XGBoost and LSTM exhibit the highest temporal stability. In relation to NN, [PERSON] [129] studies the internal and external approaches for the design of recursive neural architectures. [PERSON] et al. [130] address the problems of intelligent fault diagnosis when the data at the time of training and testing does not come from the same distribution by using domain adaptive convolutional NNs. [PERSON] et al. [131] propose an adaptive algorithm to continuously update a system of neurons through the extension of the growing neural gas algorithm with three complementary mechanisms, which allows one to closely monitor the gradual and sudden changes in the distribution of data. The imbalance data problem is addressed by [PERSON] et al. [132]. They propose the least-squares support vector machine for class imbalance learning by evaluating two parameters of misclassification costs; also, the Cholesky factorization is used to enhance computational stability. In order to reduce the estimation error in online sequential extreme learning machine systems, [PERSON] et al. [133] present a new training approach based on Kalman filter. Although the two last works have been applied to fault detection in aircraft engines, they can be used in other machines. Table 4 shows a compendium of the abovementioned methods. They are grouped by year in order to show their chronological appearance and highlight which ones are the latest algorithms or strategies proposed in the literature to solve DM issues or improve DM tasks. As these methods can address general applications, it is recommended their research and integration in fault detection methodologies of electric equipment and systems. For instance, the least-squares SVM is useful for imbalance data, i.e., when there is a disproportionate ratio of observations in each class, and the Markov chain sampling is a useful tool for mislabel data.