Penanganan imbalance class data laboratorium kesehatan dengan Majority Weighted Minority Oversampling Technique

RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique

The problem of imbalanced data, i.e., when the class labels are unequally distributed, is encountered in many real-life application, e.g., credit scoring, medical diagnostics. Various approaches aimed at dealing with the imbalanced data have been proposed. One of the most well known data pre-processing method is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE may generate ...

Class imbalance and the curse of minority hubs

Most machine learning tasks involve learning from high-dimensional data, which is often quite difficult to handle. Hubness is an aspect of the curse of dimensionality that was shown to be highly detrimental to k-nearest neighbor methods in high-dimensional feature spaces. Hubs, very frequent nearest neighbors, emerge as centers of influence within the data and often act as semantic singularitie...

Minority Challenge of Majority

C4.5 Consolidation Process: An Alternative to Intelligent Oversampling Methods in Class Imbalance Problems

In real world problems solved using data mining techniques, it is very usual to find data in which the number of examples of one of the classes is much smaller than the number of examples of the rest of the classes. Many works have been done to deal with these problems known as class imbalance problems. Most of them focus their effort on data resampling techniques so that training data would be...

A Classification Model for Imbalanced Medical Data based on PCA and Farther Distance based Synthetic Minority Oversampling Technique

Medical data are extensively used in the diagnosis of human health. So it has played a vital role for physicians as well as in medical engineering. Accordingly, many types of research are going on related to this to have a better prediction of the diseases or to improve the diagnosis quality. However, most of the researchers work on either dimensionality space or imbalanced data. Due to this, s...