# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 5 Conclusions
### Future Work  
Several promising research directions can be pursued to further refine and expand the proposed method.  
First, to address the issue of combinatorial complexity, an iterative pruning strategy could be explored. A potential strategy would be to iteratively create interim feature sets with only one less element than the total available feature set, until reaching the desired length. As an example, the amount of combinations involved in a group of 100 elements for a sample size of 5 is 75,287,520; however, the amount of interim feature sets needed by iteratively eliminating the worse-performing ones is merely 5035. This happens since, in each step, the amount of interim feature sets created is equal to the length of the available feature set. The total amount of interim sets created is equal to the sum of all elements between the maximum feature set length and the target feature length, as may be seen in Equation (2).  
\[\frac{n!}{(n-1)!(n-(n-1))!}=\frac{n\times(n-1)!}{(n-1)!}=n \tag{2}\]  
This process largely decreases the amount of interim sets needed, which may contribute to better performance [69].  
Second, the integration of additional feature extraction techniques could further increase the robustness of the proposed method. A systematic comparison of such extractors under different data contexts would help establish optimal configurations for diverse application areas.  
Third, the method could include an AutoML pipeline, enabling automated exploration of filter criteria, transformation methods, as well as combination lengths. This would enhance usability for non-expert users and reduce the need for manual tuning.  
Lastly, domain-specific applications represent a compelling area for deployment. The proposed algorithm encompasses several potential use cases for its application. One of these use cases is in bioinformatics, where datasets often contain a vast number of features, such as gene expression levels. Feature selection often leads to better results in such cases as is presented by [PERSON] in [137]. A different application of this method that may make use of its biggest advantage, its computational efficiency, is in settings where a faster (but not immediate) decision is needed, such as in medical diagnostics, and similarly to the previous case, medicine is no stranger to the use of feature selection be it due to excessive dimension or costs [4].  
Conceptualization, D.C., A.M., and I.P.; methodology, D.C., A.M., and I.P.; software, D.C.; validation, R.G., S.N., and D.A.d.O.; formal analysis, A.M., I.P., and R.G.; investigation, D.C., A.M., and I.P.; resources, D.C.; data curation, D.C.; writing--original draft preparation, D.C., A.M., and I.P.; writing--review and editing, D.C., A.M., I.P., R.G., and I.C.; visualization, D.C.; supervision, A.M., I.P., and R.G.; funding acquisition, I.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by national funds through the FCT--Fundacao para a Ciencia e Tecnologia, within PHYNHANCAI project: [[http://doi.org/10.54499/2022.01303.PTDC](http://doi.org/10.54499/2022.01303.PTDC)]([http://doi.org/10.54499/2022.01303.PTDC](http://doi.org/10.54499/2022.01303.PTDC)).  
Not applicable.  
The datasets used are described in Table 1.  
The authors [PERSON] and [PERSON] are employed by the company E-goi. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.