## 7 Conclusions and Future Works  
In this paper, we proposed an evolutionary many-objective optimization approach for simultaneously dealing with feature selection, discretization, and classifier parameter tuning for a gesture recognition task. As an illustration, the proposed problem formulation was solved using C-MOEA/DD and an LM-WLCSS classifier. In addition, the discretization sub-problem was addressed using a variable-length structure and a variable-length crossover to overcome the need of specifying the number of elements defining the discretization scheme in advance. Since LM-WLCSS is a binary classifier, the multi-class problem was decomposed using a one-vs.-all strategy, and recognition conflicts were resolved using a light-weight classifier. We conducted experiments on the Opportunity dataset, a real-world benchmark for gesture recognition algorithm. Moreover, a comparison between two discretization criteria, Ameva and ur-CAIM, as a discretization objective of our approach was made. The results indicate that our approach provides better classification performances (an 11% improvement) and stronger reduction capabilities than what is obtainable in similar literature, which employs experimentally chosen parameters, k-means quantization, and hand-crafted sensor unit combinations [19].  
In our future work, we plan to investigate search space reduction techniques, such as boundary points [27] and other discretization criteria, along with their decomposition when conflicting objective functions arise. Moreover, efforts will be made to test the approach more extensively either with other dataset or LCS-based classifiers or deep learning approach. A mathematical analysis using a dynamic system, such as Markov chain, will be defined to prove and explain the convergence toward an optimal solution of the proposed method. The backtracking variable length, \(B_{c}\), is not a major performance limiter in the learning process. In this sense, it would be interesting to see additional experiments showing the effects of several values of this variable on the recognition phase and, ideally, how it affects the NADX operator.  
Our ultimate goal is to provide a new framework to efficiently and effortlessly tackle the multi-class gesture recognition problem.  
Conceptualization, J.V.; methodology, J.V.; formal analysis, M.J.-D.O. and J.V.; investigation, M.J.-D.O. and J.V.; resources, M.J.-D.O.; data curation, J.V.; writing--original draft preparation, J.V. and M.J.-D.O.; writing--review and editing, J.V. and M.J.-D.O.; supervision,M.J.-D.O.; project administration, M.J.-D.O.; funding acquisition, M.J.-D.O. All authors have read and agreed to the published version of the manuscript.  
While performing this project, [PERSON] received a scholarship from REPARTI Strategic Network supported by Fonds quebecois de la recherche sur la nature et les technologies (FRQ-NT). This work was supported by The Natural Sciences and Engineering Research Council of Canada (NSERC) under the grant number 418235-2012 and RGPIN-2018-06329 as well as by Fond de Recherche du Quebec--Nature et Technologie (FRQ-NT) under the grant number 2016-PR-188869. We thank the REPARTI Center (strategic network) for its financial support coming from FRQ-NT.  
Ethical review and approval were waived for this study due to the open access database used in this study.  
Not applicable.  
Dataset analysed in this study is available following this link: [[https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition](https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition)]([https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition](https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition)) (accessed on 15 September 2021).  
The authors thank [PERSON] (University of Quebec at Chicoutimi) for her constructive comments and suggestions.  
The authors declare no conflict of interest.