## 3 Class Imbalance Problem
### Genetic Algorithm Architecture for Class Imbalance Problem
#### 3.4.2 Genetic Operators
behind this improvement in performance is that our objective function suppresses the weak features, hence the under represented class grows with powerful representations and results in improved classification in terms of accuracy measure. As described in Equation (18), the objective function selects the chromosome population amongst \(\alpha\) different chromosome populations. In order to evaluate the optimal value for the parameter \(\alpha\), we have provided the classification comparison over COIL and Caltech-101 datasets. The results of the comparison are presented in Figure 8:1 From the results we can observe that the algorithm gain performance benefit in case of COIL dataset with higher values of parameter \(\alpha\). Whereas, in case of the Caltech-101 dataset the performance remain constant. The foremost reason behind this performance behaviour is that if the most optimal chromosome population is reached earlier the higher values of parameter \(\alpha\) will have no impact over the retrieval performance. However, to explore the search space in a better way for obtaining the optimal chromosome population, higher values of parameter \(\alpha\) are preferred. The impact of higher values of parameter \(\alpha\) can be observed in case of COIL dataset. Due to this reason the value of parameter \(\alpha\) in our implementation is set as 500. Figure 8:2 Average accuracy over COIL and Caltech-101 datasets using different values of parameter \(\alpha\). Figure 8:3 Average accuracy over COIL and Caltech-101 datasets. ## 6 Conclusions

Application of the different classifiers for image classification and retrieval is a well established area of research. However, the classification results are not impressive when datasets suffer from imbalanced class distributions due to the class overlap and small disjuncts. An imbalanced class distribution leads the classifiers to ignore the minority class examples and resultantly lose the classification ability. The severity of the problem increases exponentially when there are several classes enrolled and there exists a high correlation amongst the classes as it is the case in the image classification. To overcome these limitations, a genetic classifier comity learning method is introduced that combines the SVM and ANN learning basis for efficient image retrieval by generating multiple versions of the classifiers through asymmetric and symmetric bagging. To elaborate the effectiveness of our technique, extensive experiments are carried out on the Corel, Caltech-101 and COIL datasets. To measure the semantic association capabilities of the proposed method, we compared it with several popular CBIR techniques. Results show that the proposed method has outperformed the comparative methods, and has also shown promising results in terms of associating images with their right semantic classes. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03933860). [PERSON], [PERSON], [PERSON], [PERSON] designed the experiments and analyzed the data. [PERSON], [PERSON] and [PERSON] have developed and implemented the algorithms. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest. ## References

* [PERSON] et al. (2000) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Content-based image retrieval at the end of the early years. _IEEE Trans. Pattern Anal. Mach. Intell._**2000**, _22_, 1349-1380. * [PERSON] et al. (2006) [PERSON]; [PERSON]; [PERSON]; [PERSON] Region-based CBIR in GIS with local space filling curves to spatial representation. _Pattern Recognit. Lett._**2006**, _27_, 259-267. * [PERSON] and [PERSON] (2006) [PERSON]; [PERSON] An interactive semantic video mining and retrieval platform--Application in transportation surveillance video for incident detection. In Proceedings of the ICDM'06, IEEE Sixth International Conference on Data Mining, Hong Kong, China, 18-22 December 2006; pp. 129-138.