# Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier
## 5 Conclusions  
The paper demonstrates an application of GSPC on a robot operation failures dataset. In the analyzed binary problem the GPSC was applied and validated for randomly selected hyperparameters, to obtain SE which can detect faulty operation. Since the binary variation of the original dataset was imbalanced the idea was to apply various balancing methods to equalize the dataset. The random oversampling and SMOTE methods were applied. The best results were evaluated on the initial dataset. After conducted investigation the following conclusions can be drawn:  
* The GPSC algorithm can be applied to obtain the models that detect the faulty operation of the robot manipulator and show high-performance metrics;
* The investigation showed that with the application of OMs, the balance between class samples was reached, and using these types of datasets in GPSC generated high-performing models. So the conclusion is that dataset OMs have some influence on the classification accuracy of obtained results;
* Conducted research demonstrating that by using a balanced dataset with the SMOTE method in the GPSC algorithm with RSHVs and 5 FCV, the best SEs in terms of high mean evaluation metric values with low standard deviation can be obtained. When the aforementioned SEs were applied to the initial imbalanced dataset the results of EMs slightly deviate from those obtained on the SMOTE dataset. * The GPSC algorithm as applied procured a set of the best SEs that can be used to obtain a robust solution;* This investigation also showed that not all variables are necessary to detect the faulty operation of a robotic manipulator. In this case, a total of 26 input variables are not required which is a great reduction in the experimental measurement of these variables. The main pros of the proposed method are:
* The entire GPSC model does not have to be stored. No matter how long the equation is it still requires lower computational resources than the entire CNN or DNN. The aforementioned CNN or DNN can not be simply transformed into a form of SE. * The generated SEs with GPSC in some cases do not require all dataset input variables. However, other machine learning methods require all input variables that were used to train them. The main cons of the proposed method are:
* The development of the RSHVs method is a time-consuming process that requires changing each hyperparameter value and running GPSC execution to investigate its influence on the performance of the algorithm;
* The ParsCoef is the most sensitive for tuning. A small change in its value can have a great impact on the GPSC performance. Future work will be focused on creating the original dataset, preferably balanced. The idea is to apply the same procedure in an experimental study to validate the mathematical equations obtained using the same procedure described in this paper. Besides that, the dataset which was used in this investigation will be subjected to a synthetic data generation process to try artificially create samples of the original dataset and after that perform oversampling. Synthetic data generation, in this case, is mandatory, especially for classes with an extremely small number of samples. Hopefully, this procedure will contribute to high classification accuracy in the multi-class problem. Conceptualization, N.A. and I.L. ; data curation, N.A. and S.B.S. ; formal analysis, I.L. and M.G. ; funding acquisition, N.A. and I.L. ; investigation, S.B.S., and M.G. ; methodology, N.A. and S.B.S. ; project administration, N.A. and I.L. ; resources, I.L. ; resources, I.L. ; software, N.A. and M.G. ; supervision, N.A. and I.L. ; validation, I.L. ; visualization, S.B.S. ; writing--original draft, N.A. and S.B.S. ; writing--review and editing, I.L. and M.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The institutional review board statement is not applicable. The informed consent statement is not applicable.