Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets

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 ...

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...

An Analysis of Classification of Imbalanced Datasets by Using Synthetic Minority Over-Sampling Technique

Abstract—Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalance...

An Improved Intrusion Detection Approach using Synthetic Minority Over-Sampling Technique and Deep Belief Network

WEMOTE - Word Embedding based Minority Oversampling Technique for Imbalanced Emotion and Sentiment Classification

Imbalanced training data always puzzles the supervised learning based emotion and sentiment classification. Several existing research showed that data sparseness and small disjuncts are the two major factors affecting the classification. Target to these two problems, this paper presents a word embedding based oversampling method. Firstly, a large-scale text corpus is used to train a continuous ...