## 1 Introduction and Motivation
### Ensemble Methods  
Ensemble methods involve the combination of data- and algorithm-based methods to overcome the problem of class imbalance [27; 28; 29; 30]. One strategy involves data sampling to reduce class noise and imbalance, followed by cost-sensitive learning or thresholding, to enable a further reduction in bias towards the majority group. Several techniques presented in Reference [22] combine ensemble methods with sampling and cost-sensitive learning. [PERSON] et al. [23] proposed two algorithms, EasyEnsemble and BalanceCascade, which learn multiple classifiers by combining subsets of the majority group with those of the minority group, to create pseudo-balanced training sets for each individual classifier. SMOTEBoost [24] introduced synthetic instances using SMOTE data preprocessing algorithms. The weights of the new instances in a dataset are proportional to the number of instances. RUSBoost [25] performs similarly to SMOTEBoost, but it removes instances from majority classes by random undersampling datasets in each iteration. Thus, it does not need to assign new weights to new instances. DataBoost-IM [31] and JOUS-Boost [32] are both examples of combining sampling with ensembles. DataBoost-IM combines AdaBoost.M1 algorithm with a data generation strategy. It identifies hard examples and then carries out a re-balanced process. [PERSON] et al. [33] introduced three cost-sensitive boosting methods (AdaC1, AdaC2, and AdaC3), which introduce cost functions to update the weights of the AdaBoost algorithm to increase the impactof the minority group. [PERSON] showed that the ensembles boosted in a cost-sensitive manner outperformed conventional boosting methods in most cases. The drawback of this method is the complexity of the cost function, which leads to a low computation speed in implementations. Bagging-based ensemble methods are developed to deal with the imbalanced problem because of its simplicity and good generalization. The concept is to obtain a useful classifier in each iteration by memorizing the importance of the diversity. In addition, some prominent methods are proposed, such as OverBagging [34], UnderBagging [35], and IIVotes [36]. [PERSON] et al. [37] reviewed the performance and complexity of some ensemble methods for the imbalanced problem. The RUSBoost and UnderBagging methods seem to be more robust than others. However, considering the computational complexity, RUSBoost is the most appropriated ensemble method. In addition, bagging techniques are commonly used because they are not only easy to develop, but also powerful when dealing with imbalanced classes. In the above-mentioned methods, models trained using an imbalanced dataset presented a pronounced inclination toward classes as the accuracy was refined using conventional learning algorithms. Thus, some researchers developed inductive classifiers to decrease the number of training-based faults by overlooking classes with a limited number of instances [38]. Inspired by ensemble methods, we developed a two-stage scheme to deal with the data imbalanced problem in the segmentation task using the PointNet network [39]. We used the PointNet deep network as the learning model because it recently became a popular method by dealing with point cloud data directly, which decreased a lot of time-consuming and efforts of data pre-processing. The novelty of our approach is to propose a framework to integrate the novel hybrid-sampling method that eases the imbalanced problem in the training of the deep segmentation network using point cloud and a novel loss function automatically assigning varying costs according to instance probabilities of classes in the batch sample. Within this framework, smaller objects with fewer data points would be assigned a higher cost-factor, whereas larger objects with a greater number of data points would be assigned a lower cost-factor. The easily implemented hybrid-sampling scheme can improve performance without enlarging the network or using additional weight parameters.