MSMOTE: Improving Classification Performance When Training Data is Imbalanced

Hu, Shengguo; Liang, Yongtu; Ma, Lintao; He, Ying

doi:10.1109/wcse.2009.756

Cited by 198 publications

(80 citation statements)

References 4 publications

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“…This process is continued for m randomly selected minority samples until the desired cardinality for minority class is achieved. Modified SMOTE (MSMOTE) [21] is one of the methods originated from SMOTE. In [22], Majority Weighted Minority Oversampling Technique (MWMOTE) is introduced which uses SMOTE algorithm for generating new samples.…”

Section: Data Preprocessing Methodsmentioning

confidence: 99%

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

Nikpour

Nezamabadi‐pour

2018

Iran J Comput Sci

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Imbalanced data sets are those in which data samples have uneven distribution amongst the classes. When classifying such data, classical classifiers encounter problem; hence, this problem has become a challenging issue in the field of machine learning. To weaken this problem, we propose a novel hyper-heuristic algorithm, called HTSS, to select the best training samples in this paper. In other words, the best training sample subset is chosen with the goal of enhancing the performance of classifier when confronting imbalanced data. To do so, some local search algorithms and a choice function are incorporated with a global search algorithm to improve its effectiveness. The global search used in this paper is binary quantum inspired gravitational search algorithm (BQIGSA) which is a recently proposed meta-heuristic search for optimization of binary encoded problems. Experiments are performed on 75 imbalanced data sets, and G-mean and AUC measures are employed for evaluation. The results of comparing the proposed method with other state of the art algorithms show the superiority of the proposed HTSS method.

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Section: Data Preprocessing Methodsmentioning

confidence: 99%

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

Nikpour

Nezamabadi‐pour

2018

Iran J Comput Sci

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“…In each iteration, the data distribution is changed by altering the weight to train the next classifier towards the positive class. These algorithms mainly include SMOTEBoost [14], RUSBoost [15], MSMOTEBoost [16], and DataBoost-IM [17] algorithms. In bagging-based ensembles, the algorithms combine bagging with data preprocessing techniques.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Yan

Han

2018

Mathematical Problems in Engineering

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Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem.

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“…Oversampling technique reduces the imbalance ratio of skewed data set by duplicating minority instances [19]. This retains all the existing instances of the training data set but causes an increase in the size of the data set [20].…”

Section: A Oversamplingmentioning

confidence: 99%

“…The under-sampling technique reduces the imbalance ratio of the skewed dataset by removing some samples from the majority class [19]. It decreases the number of samples of majority class in order to make the number of instances of two classes approximately equal.…”

Section: B Under-samplingmentioning

confidence: 99%

A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

Salunkhe¹,

Mali²

2018

IJISA

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Abstract-Customer retention is becoming a key success factor for many business applications due to increasing market competition. Especially telecom companies are facing this challenge with a rapidly increasing number of service providers. Hence there is need to focus on customer churn prediction in order to detect the customers that are likely to churn i.e. switch from one service provider to another. Several data mining techniques are applied for classifying customers into the churn and non-churn category. But churn prediction applications comprise an imbalanced distribution of the dataset.One of the commonly used techniques to handle imbalanced data is re-sampling of data as it is independent of the classifier being used. In this paper, we develop a hybrid re-sampling approach named SOS-BUS by combining well known oversampling technique SMOTE with our novel under-sampling technique. Our methodology aims to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random under-sampling. Experimental results show that the proposed approach outperforms the other reference techniques in terms of Area under ROC Curve (AUC).

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MSMOTE: Improving Classification Performance When Training Data is Imbalanced

Cited by 198 publications

References 4 publications

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

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