Imbalanced class has been a common problem encountered in the modeling process, and attracted more attention from scholars. Biased classifiers, which limit classifiers' performance for minority classes, will be produced if imbalanced ratio between number of positive labels negative is ignored. The synthetic over-sampling technique (SMOTE) very classic popular method, widely used to address this...

Abstract Oversampling is a promising preprocessing technique for imbalanced datasets which generates new minority instances to balance the dataset. However, improper generated instances, i.e., noise may interfere learning of classifier and impact it negatively. Given this, in this paper, we propose simple effective oversampling approach known as ASN-SMOTE based on k -nearest neighbors synthetic...

The current attribute reduction algorithms for information systems are difficult to handle imbalanced data with default values. Therefore, address the shortcomings of traditional (ARAs) in incomplete systems, a new algorithm is proposed by introducing intuitive fuzzy pairs (IFP). In addition, composite minority oversampling technique TampC and Central Limit SMOTE (TampC-CL-SMOTE) improve pre-da...

Penyakit jantung merupakan penyakit paling mematikan didunia. Laporan WHO tahun 2019 menyebutkan sebagai penyebab kematian tertinggi didunia dengan persentase 16% dari jumlah atau 8.9 juta kematian. Tingginya yang disebabkan oleh ini terjadi karena biasanya timbul tanpa adanya gejala sehingga sulit untuk diketahui sejak dini penderita. Salah satu cara mengatasi permasalahan tersebut adalah pema...

We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method selects minority examples from misclassified data given by an ensemble of classifiers. Then, these instances are over-sampled to create new synthetic examples using a variant of the well-known SMOTE algorithm. To build th...

Developmening systems applying machine learning and data mining techniques is one of the approaches to combating network intrusion.Many IDS Intrusion Detection Systems)suffer from a high rate of false alarms and missed intrusions.Tha challenge is to be able to improve the intrusion detection rate at a reduced false positive rate. To counter imbalance in data, a combination of oversampling (synt...