Abstract: Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders during childhood, which lasts until adulthood in most cases. In recent years, ADHD classification based on functional magnetic resonance imaging (fMRI) data has become a research hotspot. Most existing classification algorithms reported in the literature assume that samples are balanced; however, ADHD data sets are usually imbalanced. Imbalanced data sets can cause the performance degradation of a classifier by imbalanced learning, which tends to overfocus on the majority class. In this study, we considered an imbalanced neuroimaging classification problem: classification of ADHD using resting state fMRI. We used the functional connection matrix of fMRI as the classification feature and proposed a multi-objective data classification scheme based on a support vector machine (SVM) to aid the diagnosis of ADHD. In this scheme, the imbalanced data classification problem is formulated as an SVM model with three objectives: maximizing the margin, minimizing the sum of positive errors, and minimizing the sum of negative errors. Accordingly, the positive and negative sample empirical errors can be separately handled. Then, the model is solved by a multi-objective optimization method, i.e., normal boundary intersection method. A set of representative classifiers are computed for selection by decision makers. The proposed scheme was tested and evaluated on five data sets from the ADHD-200 consortium and compared with traditional classification methods. Experimental results show that the proposed three-objective SVM classification scheme is better than traditional classification methods reported in the literature. It can effectively address the data imbalance problem from the algorithm level. This scheme can be used in the diagnosis of ADHD as well as other diseases, such as Alzheimer’s and Autism.