# Simultaneous Instance and Attribute Selection for Noise Filtering
## 5 Conclusions  
In this paper, we analyzed some of the strategies for simultaneously selecting features and instances and proposed a novel method for this task. We conducted wide numerical experimentation over 15 well-known repository datasets and compared the overall performance of several methods in different scenarios.  
Our experimental analysis shows that using error-based editing methods contributes to classifier performance. In addition, using a combination of error-based editing followed by a condensation algorithm leads to highly accurate training sets with fewer instances. However, using evolutionary preprocessing can lead to significant drops in classifier accuracy. Regarding the proposed algorithm, it had the best results by having a good tradeoff between reduction in both features and instances, as well as keeping classifier accuracy as high as possible.  
The ROFS method filters noisy instances and focuses on the correct classification of misclassified objects. This method is also deterministic, and it is able to deal with hybrid and incomplete data, as well as using any supervised classifier. Our method outperformed others according to classifier error, retaining a significantly smaller number of instances and features. We believe the main reason ROFS obtained such good results is related to the computation of candidate attribute sets, the use of error-based editing  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Methods** & **Parameters** \\ \hline AKH-GA & population: 200, iterations: 50, crossover probability: 0.7, mutation probability: 0.1 \\ DS & None \\ IN-GA & population: 50, iterations: 500, crossover probability: 1.0, mutation probability from 0 to 1: 0.1, mutation probability from 1 to 0: 0.01 \\ KJ-GA & population: 10, iterations: 100, crossover probability: 1.0, mutation probability: 0.1 \\ RMHC-FP1 & population: 10, iterations: 100 \\ ROFS & CA: RoPM [33], Cond: CNN [34], _classf_: 1-NN \\ SOFSA & None \\ TCCS & None \\ \hline \hline \end{tabular}
\end{table}
Table 10: Parameters of the compared preprocessing methods.  
plus a condensing algorithm, and the intelligent fusion procedure focused on the correct classification of instances.  
In future work, we want to explore the use of strategies for making ROFS suitable to deal with big data, such as parallel computation of candidate attribute sets. In addition, we want to test other condensation methods besides CNN to evaluate the impact of specific techniques on the results of the ROFS preprocessing.  
The supporting information can be downloaded at: [[https://github.com/yenny-villuendas/ROFS](https://github.com/yenny-villuendas/ROFS)]([https://github.com/yenny-villuendas/ROFS](https://github.com/yenny-villuendas/ROFS)) (accessed on 15 September 2024).  
Conceptualization, Y.V.-R.; methodology, Y.V.-R.; software, Y.V.-R.; validation, O.C.-N., C.C.T.-R. and Y.V.-R.; formal analysis, O.C.-N.; investigation, C.C.T.-R.; data curation, C.C.T.-R. and Y.V.-R.; writing--original draft preparation, Y.V.-R.; writing--review and editing, O.C.-N.; visualization, C.C.T.-R.; supervision, Y.V.-R. and O.C.-N.; project administration, Y.V.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this research are publicly available at: [[https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)]([https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)) (accessed on 14 April 2024).  
The authors would like to thank the Instituto Politecnico Nacional (Secretaria Academica, Comision de Operacion y Fomento de Actividades Academicas, Secretaria de Investigacion y Posgrado, Centro de Innovacion y Desarrollo Tecnologico en Computo, and Centro de Investigacion en Computacion), the Consejo Nacional de Humanidades, Ciencia y Tecnologia (CONAHCYT), and Sistema Nacional de Investigadores (SNII) for their support in developing this work.  
The authors declare no conflicts of interest.