## 2 Related Work  
The class imbalance problem is one of the many challenges that the machine learning community is facing [14; 16]. This imposes a critical situation to address, as many real world classification problems (including CBIR) are seriously suffering from it. To improve the classification results, several research works are accounting this issue.  
[PERSON] et al. proposed a cross-category label propagation algorithm to enhance image retrieval performance in the presence of small sample sets [17]. The algorithm can directly propagate the inter-category knowledge at the instance level between source and target categories. Furthermore, the algorithm can automatically detect conditions under which the transfer process may become detrimental to the learning process, so it can avoid the propagation of the knowledge from those categories.  
[PERSON] et al. presented a null space locality preserving the discriminant analysis method for facial recognition [18]. The method projected the training samples into a scatter matrix that are then randomly sampled through bagging to generate a set of bootstrap replicas. The overall goal of the technique was to improve the face recognition performance by alleviating the over-fitting due to large null spaces.  
[PERSON] et al. presented a conformal kernel transformation for SVM to address the class imbalance problem [19]. For this an approximated hyperplane of the SVM was obtained and a kernel function was applied by computing the parameters through the chi-square test. [PERSON] et al. improved the association capability of SVM by surpassing the approximate solution through kernel transformation [20]. The asymmetric space around the class boundary was enlarged that compensates the data skewness in binary classification. [PERSON] et al. addressed the issue of imbalanced sets by generating multiple versions of SVM classifier by replacing negative samples with the positive duplicates [21]. Improvement in the image retrieval occurs with this approach but it is not significant when feedbacks are severely imbalanced. The similar approach was followed in [22] with expectation maximization (EM) for parameter estimation in relevance feedback (RF)-based CBIR. [PERSON] et al. modified the kernel matrix of the SVM to address the bi-class imbalance problem [23].  
In [24], performance refinements of AdaBoost were exhibited on imbalanced datasets. As a first step, the dataset was over-sampled and the standard AdaBoost was applied. In the next step, the parameters of classifiers were obtained through GA. The sampling and re-weighing strategy tuned the AdaBoost towards a given performance measure of interest by not exceeding the computational overhead. [PERSON] et al. performed feature selection through GA and linear discriminant analysis (LDA) for dermatological image retrieval [25]. The algorithm evolves the feature vectors through the binary-GA and performs feature selection through LDA that are then classified through the k-nearest neighbors (KNN) classifier. [PERSON] et al. proposed a GA-based simultaneous classifier for image annotation and region label assignment [26]. The algorithm represents various image regions through feature vectors and selects the salient features through GA that are then used to train the multi-classifier ensembles for image annotation. [PERSON] et al. used GA for increasing the relevant output for semantic webs [27]. The system considers the user search preferences and applies GA to improve the output after multiple feedback rounds.  
Most of the described techniques were designed for dealing with the class imbalance problem that spans only to the two-class scenarios. However, the application of bi-class resolutions usually appear to be less effective or even cause a negative impact when implied in the multi-class situations [28].