# Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier
## 1 Introduction
The representation of all research papers that used the dataset [18] in their investigation with various ML algorithms and achieved classification accuracy (\(ACC\), \(F1-score\)) values are presented in Table 1.  
It can be seen from Table 1 that in none of the research papers was the genetic programming-symbolic classifier (GPSC) used. \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Methods** & **Results** \\ \hline
[19] & GA-SOM, SOM & \(ACC:91.95\%\) \\ \hline  & Naive Bayes, Boosted Naive Bayes, & \\  & Bagged Naive Bayes, SVM, & \\  & Boosted SVM, Bagged SVM, & \\  & Decision Table (DT), Boosted DT, & \\  & Bagged DT, Decision Tree (DTr), & \(ACC:95.24\%\) \\  & Boosted (DTr), Bagged DTr, & \\  & Plurality Voting, & \\  & Stacking Meta Decision Trees, & \\  & Stacking Ordinary Decision Trees & \\ \hline
[21] & DCNN & \(ACC:98.82\%\) \\ \hline
[22] & NN with & \(ACC:95.45\%\) \\  & Bayesian regularization & \\ \hline
[23] & MLP & \(ACC:90.45\%\) \\ \hline  & DBN, C-support vector classifier, & \\  & logistic regression, decision tree classifier, & \\  & K-Nearest Neighbor Classifier, & \\  & MLP, AdaBoost Classifier, & \\  & Random Forrest Classifier, & \\  & Bagging Classifier, Voting Classifier & \\ \hline
[3] & SNN & \(F1-Score:1.00\) \\ \hline \end{tabular}
\end{table} 
Table 1: The list of research papers, with described ML methods and achieved results in fault operation obtained using dataset [18]. _The Description of Research Novelty, Investigation Hypotheses with Overall Scientific Contribution_  
It can be noticed from the previously presented state of the art, that there are a few papers in which ML algorithms were used for fault operation analysis and detection. The main disadvantage which can be noticed in the previous literature overview is that these ML models are computationally intensive, i.e., they require large computational resources, especially the CNN or DNN. After these algorithms are trained on a specific dataset they have to be stored to be used in the future for processing new data. Storing and re-using these models requires a lot of computational resources so their usage in systems that control the robot manipulators is questionable. Another problem with the previously mentioned algorithms is that it is nearly impossible to express the complex models obtained by them as comparatively simple mathematical expressions which are understandable to scientists and engineers. The research novelty is to present the process of the GPSC algorithm implementation to obtain symbolic expressions (SEs) which can detect fault operation with high classification performance. The SEs are easier to implement in existing control systems of robotic manipulators since they have lower computation-wise performance requirements when compared to complex models such as CNN or DNN. The main idea of this research is the procedure presentation of the GPSC algorithm application to generate SEs which will be utilized as high-performance detection models of robot manipulator faults. The algorithm used in this paper [25] begins its process by creating the population members of the initial population that are not able to solve a particular task. However, through the predefined number of generations with the application of evolutionary computing operations (recombining and mutating), they adapt as the solution for a particular task. From state-of-the-art, defined novelty, and research ideas, the following hypotheses will be investigated in this paper:  
* Is there a possibility to use the GPSC algorithm to generate SEs for the detection of fault operation of a robotic manipulator with high classification performance? * Can the proposed algorithm achieve high classification performance on datasets that are balanced using various oversampling methods? * Is it possible to use the GPSC algorithm with a random selection of hyperparameter values (RSHV) method, validated with 5-fold cross-validation (5 FCV) to obtain SEs for the detection of fault operation of a robotic manipulator with high classification accuracy?