# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 6 Conclusions and Future Works  
In this paper, an in-depth study was carried out on data balancing techniques, as well as their application in financial data, and their impact in the performance of associative classifiers. This study allowed us to reach the following conclusions:  
1. About sampling methods: 1. All of the oversampling methods tested obtained balanced datasets, although at the cost of increasing the cardinality of the data. 2. The undersampling methods analyzed (CNN, CNNTL, NCL, OSS, SBC and TL), but RUS, fail to find balanced data sets, when the imbalance ratio of the original set was greater than 4.0. However, for moderate imbalance ratios (less than 4.0), the NCL and TL algorithms got good results. 3. Systematically, the CNN, CNNTL, OSS and SBC algorithms reversed the amounts of instances in the classes in the datasets, making the majority class a minority. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**i** & **Algorithm** & **z** & **P** & **Holm** \\ \hline
6 & SBC & 2.950484 & 0.003173 & 0.008333 \\ \hline
5 & CNNTL & 2.314995 & 0.020613 & 0.010000 \\ \hline
4 & Original & 2.088035 & 0.036795 & 0.012500 \\ \hline
3 & OSS & 1.679506 & 0.093053 & 0.016667 \\ \hline
2 & TL & 0.726273 & 0.467671 & 0.025000 \\ \hline
1 & NCL & 0.090784 & 0.927664 & 0.050000 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Results of the Holm test comparing the performance of the SNDAM classifier after undersampling algorithms. The test rejects the hypothesis having an unadjusted \(p\)-value \(\leq\) 0.01. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**i** & **Algorithm** & **z** & **P** & **Holm** \\ \hline
4 & Original & 4.279198 & 0.000019 & 0.012500 \\ \hline
3 & SPIDER & 2.914816 & 0.003559 & 0.016667 \\ \hline
2 & SPIDER2 & 2.790782 & 0.005258 & 0.025000 \\ \hline
1 & SMOTE-TL & 1.17833 & 0.238665 & 0.050000 \\ \hline \hline \end{tabular}
\end{table} 
Table 27: Results of the Holm test comparing the performance of the SNDAM classifier after hybrid algorithms. The test rejects the hypothesis having an unadjusted \(p\)-value \(\leq\) 0.05. The SBC algorithm had a very bad behavior in the face of financial data, since it systematically eliminated all the instances of the majority class. Both SMOTE-ENN and SMOTE-TL obtained good results according to data balancing. SPIDER2 obtained better balanced datasets than SPIDER. About the impact of the sampling in the associative classifiers:  
The HACT, Extended Gamma and NAC classifiers do not benefit from financial data balancing. Undersampling algorithms do not benefit the SNDAM classifier. However, oversampling and hybrid methods do increase the performance of SNDAM over imbalanced financial data. There is a significant improvement, within a 95% of confidence, in the Area under the ROC curve of SNDAM while sampling imbalanced financial data by SMOTE and SMOTE-ENN. Considering the above, it is proposed as future work of the research:  
1. To design undersampling algorithms that are robust to high imbalance ratios, in order to solve the limitations founded in the evaluated algorithms. 2. To apply the proposed methodology to other supervised classifiers, for instance Deep Neural Networks and other algorithms related with Deep Learning. 3. To choose other datasets, related to areas of interest other than financial, in order to perform experiments similar to those presented in this paper. Conceptualization, C.Y.-M. and Y.V-R.; methodology, Y.V.-R.; software, A.R.-D.-d.-l.-V.; validation, O.C.-N., formal analysis, Y.V.-R. and C.Y.-M.; investigation, I.L.-Y. ; writing--original draft preparation, Y.V.-R.; writing--review and editing, C.Y.-M.; visualization, A.R.-D.d.-l.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding.