LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
نویسندگان
چکیده
منابع مشابه
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 ...
متن کاملSMOTE: Synthetic Minority Over-sampling Technique
An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (i...
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Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification a...
متن کاملA 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...
متن کاملA Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning
Imbalanced class distribution is a challenging problem in many real-life classification problems. Existing synthetic oversampling do suffer from the curse of dimensionality because they rely heavily on Euclidean distance. This paper proposed a new method, called Minority Oversampling Technique based on Local Densities in Low-Dimensional Space (or MOT2LD in short). MOT2LD first maps each trainin...
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ژورنال
عنوان ژورنال: BioData Mining
سال: 2013
ISSN: 1756-0381
DOI: 10.1186/1756-0381-6-16