## 3 Class Imbalance Problem
### Genetic Algorithm Architecture for Class Imbalance Problem
#### 3.4.2 Genetic Operators
[47], [PERSON] et al. [48], [PERSON] et al. [49], [PERSON] et al. [50], [PERSON] et al. [51], [PERSON] et al. [52] and [PERSON] et al. [53]. Table 2 presents the class-wise comparison of the proposed system with comparative systems in terms of average precision values. Similarly, Table 3 presents a comparison of the proposed system with comparative systems in terms of average recall values. From the results it can be observed that our proposed system is showing promising precision and recall rates in many categories and has the highest overall precision and recall values against the comparative systems (Tables 2 and 3). To further elaborate the retrieval capabilities of the proposed method, we have also plotted the precision of the proposed method on different output rates, i.e., from the top 20 to 60 images. For this experiment we run the simulation with same parameter settings as described above, by randomly selecting 200 images (20 images from each image category of Corel A) and running the simulation for five times. The results of the simulation are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Proposed**} & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\  & & [45] & [46] & [47] & [48] & [49] & [50] & [51] & [52] & [53] \\ \hline Africa & 0.83 & 0.75 & 0.67 & 0.85 & 0.74 & 0.44 & 0.72 & 0.51 & 0.55 & 0.72 \\ Beach & 0.72 & 0.55 & 0.60 & 0.53 & 0.58 & 0.32 & 0.65 & 0.90 & 0.63 & 0.59 \\ Buildings & 0.86 & 0.67 & 0.56 & 0.72 & 0.62 & 0.52 & 0.70 & 0.58 & 0.67 & 0.58 \\ Buses & 1.00 & 0.95 & 0.96 & 0.85 & 0.80 & 0.60 & 0.89 & 0.78 & 0.84 & 0.89 \\ Dinosaurs & 0.97 & 0.97 & 0.98 & 1.00 & 1.00 & 0.40 & 1.00 & 1.00 & 0.89 & 0.99 \\ Elephants & 0.82 & 0.63 & 0.53 & 0.68 & 0.75 & 0.80 & 0.70 & 0.84 & 0.77 & 0.70 \\ Flowers & 0.86 & 0.93 & 0.93 & 0.94 & 0.92 & 0.57 & 0.94 & 1.00 & 0.90 & 0.92 \\ Horses & 0.82 & 0.89 & 0.82 & 0.99 & 0.89 & 0.75 & 0.91 & 1.00 & 0.81 & 0.85 \\ Mountains & 0.69 & 0.45 & 0.46 & 0.55 & 0.56 & 0.57 & 0.72 & 0.84 & 0.71 & 0.56 \\ Food & 0.90 & 0.70 & 0.58 & 0.86 & 0.80 & 0.56 & 0.78 & 0.38 & 0.71 & 0.77 \\ \hline
**Mean** & **0.847** & **0.749** & **0.709** & **0.797** & **0.766** & **0.553** & **0.801** & **0.783** & **0.748** & **0.757** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of average precision obtained by proposed method and other standard retrieval systems on top 20 retrievals. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Proposed**} & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\  & & [45] & [46] & [47] & [48] & [49] & [50] & [51] & [52] & [53] \\ \hline Africa & 0.17 & 0.15 & 0.13 & 0.17 & 0.15 & 0.09 & 0.14 & 0.10 & 0.11 & 0.14 \\ Beach & 0.14 & 0.11 & 0.12 & 0.11 & 0.12 & 0.06 & 0.13 & 0.18 & 0. ### Bagging Impact

We have also compared the proposed method against the SVM and ANN classifiers to show the performance benefits of GCCL against the individual classifiers. For this the proposed GCCL based system is compared against SVM and ANN classifiers in terms of precision and false positive rates. The reason to use the false positive rates as performance measure is that the lower values of false positive rates are also required for higher classification accuracies. The results of the comparison are presented in Figures 7 and 8. From the results we can observe that the proposed GCCL based scheme as generates the powerful classifier by overcoming the drawbacks of the weak classifiers hence is much effective and precise in terms of image retrieval. ### Performance Evaluation in Imbalanced Domains

The evaluation criteria is the most significant thing for the classifier assessment.