The paper presents two rough sets based filtering approaches combined with rule based classifiers suited for handling imbalanced data sets, i.e., data sets where the minority class of primary importance is under-represented in comparison to the majority classes. We introduced two techniques to detect and process inconsistent majority cases in the boundary between the minority and majority class...

Abstract Early diagnosis plays a key role in prevention and treatment of skin cancer. Several machine learning techniques for accurate detection cancer from medical images have been reported. Many these are based on pre-trained convolutional neural networks (CNNs), which enable training the models limited amounts data. However, classification accuracy still tends to be severely by scarcity repr...

The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on large-scale data sets because of such reasons as the complexity and number of iterations in parameter fitting methods, underlying optimization solvers, and nonlinearity of kernels. We introduce a fast multilevel framew...

The paper addresses some theoretical and practical aspects of data mining, focusing on predictive data mining, where two central types of prediction problems are discussed: classification and regression. Further accent is made on predictive data mining, where the time-stamped data greatly increase the dimensions and complexity of problem solving. The main goal is through processing of data (rec...

Following recent results [9 , 8] showing the importance of the fatshattering dimension in explaining the beneficial effect of a large margin on generalization performance, the current paper investigates the implications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting algorithm ...

There is an overwhelming increase in submissions to genomic databases, posing a problem for database maintenance, especially regarding annotation of fields left blank during submission. In order not to include all data as submitted, one possible alternative consists of performing the annotation manually. A less resource demanding alternative is automatic annotation. The latter helps the curator...

The performance of traditional classifier skews towards the majority class for imbalanced data, resulting in high misclassification rate for minority samples. To solve this problem, a combination classification algorithm based on outlier detection and C4.5 is presented. The basic idea of the algorithm is to make the data distribution balance by grouping the whole data into rare clusters and maj...

In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic ...