Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended the basic FRBCS in order to decrease the side effects of imbalanced data by employing data-mining criteria such asconfidence and support. These measures are computed from information derived from data in the sub-spaces of each fuzzy rule. The experimental results show that the proposed method can improve the classification accuracy when applied on benchmark data sets.

Through the use of malware, particularly JavaScript, cybercriminals have turned online applications into one of their main targets for impersonation. Detection of such dangerous code in real-time, therefore, becomes crucial in order to prevent any harmful action. By categorizing the salient characteristics of the malicious code, this study suggests an effective technique for identifying malicious Java scripts that were previously unknown, employing an interceptor on the client side. By employing the wrapper approach for dimensionality reduction, a feature subset was generated. In this paper, we propose an approach for handling the malware detection task in imbalanced data situations. Our approach utilizes two main imbalanced solutions namely, Synthetic Minority Over Sampling Technique (SMOTE) and Tomek Links with the object of augmenting the data and then applying a Deep Neural Network (DNN) for classifying the scripts. The conducted experiments demonstrate the efficient performance of our approach and it achieves an accuracy of 94. 00%.

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes which is known to deteriorate the performance of classifiers. It also influences its validity and generalizablity. The classification models in this study were built using five machine learning algorithms known as PLS-DA, MLP, SVM, C4. 5 and ID3. This model is built after carrying out a number of intensive data preprocessing procedures to tackle the problem of imbalanced classes and improve the performance of the constructed classifiers. These procedures involves applying data transformation, normalization, standardization, re-sampling and data reduction procedures using a number of variables importance scorers. The best performance was achieved by building an MLP model that was trained and tested using five-fold cross-validation using datasets that were re-sampled using SMOTE method and then reduced using SVM variable importance scorer. This model was successful in classifying samples with excellent accuracy and also in identifying the potential disease biomarkers. The results confirm the validity of metabolomics data mining for diagnosis of cachexia. It also emphasizes the importance of data preprocessing procedures such as sampling and data reduction for improving data mining results, particularly when data suffers from the problem of imbalanced classes.

Credit scoring is an important topic, and banks collect different data from their loan applicant to make an appropriate and correct decision. Rule bases are of more attention in credit decision making because of their ability to explicitly distinguish between good and bad applicants. The credit scoring datasets are usually imbalanced. This is mainly because the number of good applicants in a portfolio of loan is usually much higher than the number of loans that default. This paper use previous applied rule bases in credit scoring, including RIPPER, OneR, Decision table, PART and C4.5 to study the reliability and results of sampling on its own dataset.A real database of one of an Iranian export development bank is used and, imbalanced data issues are investigated by randomly Oversampling the minority class of defaulters, and three times under sampling of majority of non-defaulters class. The performance criterion chosen to measure the reliability of rule extractors is the area under the receiver operating characteristic curve (AUC), accuracy and number of rules. Friedman’s statistic is used to test for significance differences between techniques and datasets. The results from study show that PART is better and good and bad samples of data affect its results less.