## 6 Conclusions and Future Works  
In this paper, we briefly studied the selection of optimized wavelet family and decomposition level by the FAPSO algorithm as the feature representation method and the virtual class method as the hierarchical classification method for GPCR protein. Based on the results, the most selected wavelet family and decomposition level chosen to represent GPCR classes by FAPSO were Biorthogonal wavelets and decomposition level 1. The choice of an adequate feature extraction strategy for protein classification is an important problem because they influence the performance measurement values according to the commonly used machine learning algorithm. It can be seen that virtual class consistently increases the performances in all experiments since it increases the likelihood of avoiding error propagation in the hierarchy by stopping the classification at an appropriate level. This area of research still has a lot of space for growth and advancement. Data mining always runs into the imbalance data problem in the data set. A similar issue, with 60% of known GPCR protein sequences being proteins from the A family, also affects the GPCR dataset. To solve this issue, sampling techniques such as the Synthetic Minority Oversampling Technique (SMOTE) algorithm must be taken into account. SMOTE is an oversampling method that creates artificial samples from minority classes. After obtaining a synthetically balanced training set for each class, the classifier is trained. Future work should investigate the best wavelet family and level of decomposition to represent the GPCR protein class. The trials carried out in this study used parameter values for the FAPSO algorithm based on prior work. The suitability of the parameters for FAPSO is not examined in this work. This necessitates a thorough investigation to establish the correct parameter values needed for the FAPSO algorithm as an algorithm for choosing the wavelet family and decomposition level. There are various implementation strategies for hierarchical classification. This includes global hierarchical classification, which does not have the same issue with errors propagating as in the present study. A training set for global classification is used to create a sophisticated classification model. When classifying, this takes into account the entire class structure. Each test data set will be categorised using the test phase's classification model. This global hierarchical classification method is expected to enhance the performance of the hierarchical classification of GPCR proteins. Conceptualization, N.A.M.K. ; methodology, N.A.M.K. ; software, N.A.M.K. ; validation, N.A.M.K., A.A.B. and S.Z. ; formal analysis, N.A.M.K. and A.A.B. ; investigation, N.A.M.K. and A.A.B. ; resources, N.A.M.K. ; writing--original draft preparation, N.A.M.K. ; writing--review and editing, N.A.M.K., A.A.B. and S.Z. ; visualization, N.A.M.K. ; supervision, A.A.B. and S.Z. ; project administration, A.A.B. and S.Z. ; funding acquisition, A.A.B. and S.Z. All authors have read and agreed to the published version of the manuscript. This study was financially supported by the Malaysia Ministry of Higher Education (MOHE) and Universiti Kebangsaan Malaysia-TAP-K012964, GUP-2020-089. Not applicable. Data supporting the proposed approaches in this study were collected from [[https://www.cs.kent.ac.uk/projects/biasprofs/downloads.html](https://www.cs.kent.ac.uk/projects/biasprofs/downloads.html)]([https://www.cs.kent.ac.uk/projects/biasprofs/downloads.html](https://www.cs.kent.ac.uk/projects/biasprofs/downloads.html)) (accessed on 23 March 2014). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
WT Wavelet Transform  
DWT Discrete Wavelet Transform  
SVM Support Vector Machine  
PSO Particle Swarm Optimization  
FA Firefly Algorithm  
FAPSO Firefly Algorithm-Particle Swarm Optimization  
VC Virtual Class  
DB Daubechies  
Coifi Colifet  
Sym Symlet  
Dmey Discrete Meyer  
FK Fejer-Korovkin  
Bior Bior Biorthogonal  
Reverse Biorthogonal