# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 2 Related Works  
Over the years, the research on imbalanced classification mainly focuses on data resampling and ensemble learning. Data resampling belongs to data-level method, which consists of balancing the original datasets and using oversampling or undersampling strategy to reduce the imbalance ratio. Oversampling refers to generate minority class samples artificially to maintain data balance. The most well-known oversampling method is synthetic minority oversampling technique (SMOTE) proposed by [PERSON] et al. [15]. The main idea of SMOTE [15; 16; 17; 18; 19] is to identify k minority class neighbors close to each minority class sample, then randomly select a point between the sample and its neighbors as the synthetic sample. But SMOTE produces new samples with certain blindness and may make class overlapping more serious. After that, researchers combined many methods with SMOTE to improve the performance of the algorithm, such as Borderline-SMOTE [17] and so on. ADASYN (adaptive synthetic sampling) [18] is also an effective oversampling method, which is characterizedby using a mechanism to automatically determine the number of synthetic samples needed for each minority sample, rather than synthesizing the same number of samples for each minority sample like SMOTE. Therefore, ADASYN is used as the oversampling method in this paper. Undersampling [12; 13] maintains data balance by deleting the majority samples, such as RUS (Random Undersampling). The main idea of RUS is to randomly remove some samples from the majority samples, and then constitute a new training set with minority samples, but it is easy to lose important information in the data. Clustering is a kind of unsupervised learning, which is used to process data by many researchers [10; 11; 12; 13; 14]. [PERSON] et al. [10] proposed a clustering algorithm based on K-means paradigm, which is suitable for data with mixed numeric and categorical features. The combination of clustering and resampling [12; 13; 14; 15] tends to produce better results. CBU (Clustering-based Undersampling) proposed by [PERSON] et al. [12] combines K-means and undersampling strategy, K-means only clusters the majority class and the number of clusters is same as that of the minority samples, and then CBU uses the nearest neighbor of each cluster center to represent the whole cluster and combines them with the minority samples to form a balanced training set. Although it improves the classification results effectively, it may still ignore some information of the majority class because it only selects one sample of each cluster. In this paper, EKR is an improvement based on CBU and gets better performance than CBU. Ensemble learning is one of the most popular methods at present. It has near-optimal classification methods for any problem, and it can achieve better generalization performance than a single classifier by training multiple individual classifiers and combining them together [8; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. There are two main approaches of ensemble learning: Bagging and Boosting. [PERSON] et al. [22] proposed a novel classifier ensemble framework, named CSBC (classifier selection based on clustering), CSBC uses Bagging to produce base classifiers and partitions them by using a clustering algorithm. Then CSBC produces a final ensemble by selecting one classifier from each cluster. [PERSON] [23] proposed an ensemble-based approach for feature selection in order to overcome the problem of parameter sensitivity of feature selection approaches. In many cases, it is better to combine resampling with ensemble learning [26; 27; 28; 29; 30; 31; 32]. [PERSON] et al. [26] proposed EUS (ensemble undersampling), which selects the same number of samples from the majority class as the minority class to form several balanced subsets, and trains SVM-based classifiers for each subset to overcome the problem of information loss in undersampling to a certain extent.