Rail defect detection by video cameras has recently gained much attention in both academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning techniques. In this paper we investigate if positive defective candidates selected from the unlabeled data can...

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...

The classification problem is one of the important research subjects in the field of machine learning. However, most machine learning algorithms train a classifier based on the assumption that the number of training examples of classes is almost equal. When a classifier was trained on imbalanced data, the performance of the classifier declined clearly. For resolving the class-imbalanced problem...

With the urbanization process around the globe, traffic accidents have undergone a rapid growth in recent decades, causing significant life and property losses. Predicting traffic accidents is a crucial problem to improving transportation and public safety as well as safe routing. However, the problem is also challenging due to the imbalanced classes, spatial heterogeneity, and the non-linear r...

China’s bond market is an emerging market. The number of defaults has been increasing in recent years, but the data set severely imbalanced. Based on financial total 6731 corporate issuers which 50 had defaulted, this paper uses XGboost algorithm and Over-sampling method named SMOTE to predict default issuers. results show that advantages over traditional processing imbalanced data, one effecti...

Associative Classification (AC) is a well known tool in knowledge discovery and it has been proved to extract competitive classifiers. However, imbalanced data has posed a challenge for most classifier learn ing algorithms including AC methods. Because in the AC process, Interestingness Measure (IM) p lays an important role to generate interesting rules and build good classifiers, it is very im...

Most medical datasets are not balanced in their class labels. Furthermore, in some cases it has been noticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without...