# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 2 Related Work
It can be found that their experimental results have demonstrated that they have better performances in AD classification and prediction, owing to the multi-feature fusion/selection strategies. However, since the above work did not consider the characteristics of imbalanced data distribution, their performance of prediction still has room for further improvement. According to a survey of the related literature, it was shown that there exist few studies on imbalanced MRI AD, and most AD-related machine learning work is based on the hypothesis of balanced data distribution. However, the number of people with AD is usually less than the normal number. In [37], due to the imbalance in the number of subjects in NC and MCI groups, they achieved a much lower sensitivity than specificity. In addition, it is commonly agreed that imbalanced datasets adversely impact the performance of the classifier as the learned model is biased towards the majority class to minimize the overall error rate [38; 39]. In the view of the above analysis, two issues must be considered for the imbalanced AD dataset. One is the feature extraction for images, and the other is how to deal with data distribution imbalance. Since our work in this paper is to address the problem of data imbalance by improving the existing methods, that is why we follow the idea of [20], a multi-feature fusion during feature extraction. In feature extraction, VBM can effectively distinguish gray matter changes in the brain region [10] and is often widely used in the study of AD [7; 40; 41]. Moreover, considering the complexity of AD and to extract more feature information, we extract the morphological features and texture features to analyze the imbalanced AD image data. However, there are often some redundant, irrelevant, and less effective features among morphological features and texture features. To deal with AD imbalance learning problems, there are many researchers devoted to this area and who have conducted comprehensive investigations, which are mainly divided into internal solutions and external solutions [42]. Internal methods solve the imbalance problem by designing new algorithms or improving existing algorithms [43]. Because of the complexity of high dimensional imbalanced data, it is very difficult to find a classifier that can directly classify and meet users' requirements. For instance, different penalties are assigned to different class labels in the support vector machine-based classifiers [44]. The external method reduces the impact of imbalance on the classification by pre-processing the data [45; 46; 47]. This method directly changes the imbalanced distribution of the data by employing a sampling technique; for example, [PERSON] et al. [48] introduced a novel undersampling approach called cluster-based instance selection (CBIS) that combines clustering analysis and instance selection; [PERSON] et al. [49] used the SMOTE technique to address the class imbalance problem in the dataset, by randomly duplicating the minority class of images in the dataset to minimize the overfitting problem; [PERSON] et al. [50] focused on providing an individualized MCI to AD conversion prediction using a balanced random forest model, and the oversampling is performed to balance the initially imbalanced classes before training the model with 1000 estimators; and [PERSON] et al. [51] employed a transfer learning-based technique using data augmentation for 3D MRI views to avoid the class imbalance problem. Because some samples will be replicated in the oversampling dataset, the trained model will be subject to a certain degree of overfitting. In contrast, undersampling will lead to the loss of some data in the final training set, resulting in the model acquiring only a portion of the overall pattern. To address the pre-processing of high-dimensional imbalanced data, it is necessary to propose a novel solution. As mentioned above, an important goal of AD research is to identify key biometric signatures.