# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Discussion  
Many experiments were performed in this paper to fulfill the four reseach objectives. Firstly, the related works were deeply investigated to bring out their limitations. Consequently, the first objective was achieved.  
To prove the efficacy of these four PPFS, nineteen datasets and three classifiers were utilized. The first experiment consists of showing the detected outliers for the nineteen datasets using the four FS methods. It was shown that the PPFS methods provide equivalent results in terms of the detecting outliers. In addition, the detection frequency of most of the outliers reached 80% using the four PPFS methods. This confirms that they are outliers. To show that the detected outliers correspond to non-relevant features, classification was performed on the datasets before and after applying the four PPFS methods. The results showed that the classification performance along with the precision, the recall, and the F1 measure were superior when applying PPFS. The k-NN achieved an improvement, reaching 6.79 for 11 datasets. The SVM and RN enhanced the results of 16 datasets, with an improvement reaching 12.91 and 12.4, respectively. Hence, SVM was selected as the best classifier for this study. Although the proposed PPFS did not improve the accuracy of the Waveform and Segment datasets, they enhanced the classification accuracy for most of the datasets. This allowed the second and third objectives to be realized.  
On the other hand, the success of this study could not be realized without the utilization of EPP-Lab software. Therefore, EPP-Lab is efficient in detecting the non-important features. This software is open-source and can be used as a desktop application in any environment. This is the fourth objective of the present study.  
One of the main experiments performed in this article was to show that the four presented PPFS methods provide equivalent results. This was carried out through the Friedman test. The test's result indicated that the _p-value_ is greater than the significace level and that the effect size is small. Consequently, the hypothesis is not rejected, and hence the difference between the PPFS results is not statistically significant. This experiment realized the fifth objective.  
To demonstrate that the four PPFS are competitive FS methods, two comparison studies were performed. The first comparison included applying five filter FS methods (CFS, PCA, Gain Ratio, Mutual Information, Gini Index) using all the datasets. The results showed that CFS was superior to the four PPFS methods when testing only the Sonar dataset. PCA was also superior to the four PPFS methods when testing both the Waveform and Sonar datasets. Again, Mutual Information and Gini Index outperformed the PPFS for the Spam dataset. As stated previously, these three datasets have noise and high correlation, which negatively affect PPFS in enhancing the classification accuracy. These limitations deserve further investigation. The classification accuracy results of PPFS-GAK and the five Filter FS methods were used to compute the Friedman and pairwise Wilcoxon tests. The statistical results show that GAK is superior to Mutual Information, Gain Ratio, and Gini Index but equivalent to PCA and CFS.  
The second comparison involved comparing PPFS with three wrapper FS methods, called WOA, PSO, and GA, using eight datasets. The four PPFS methods performed better than the three wrapper FS using five datasets. However, WOA surpassed all the FS methods using Sonar, Waveform, and Colon datasets. Again, the PPFS methods failed in enhancing the accuracy of these three datasets. Like the first comparison study, the statistical analysis validated the significant results of the PPFS methods. Finally, the last objective was achieved by demonstrating the efficiency of the PPFS methods. To sum up, the aforementioned experiments successfully carried out the research objectives of this study.  
In the following, the research impacts, the advantages, and the limitations of PPFS methods are discussed.