Abstract Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide important information in experimental results. However, we find that analysis for performance evaluation and what they can or reveal is rarely covered related works. Therefore, address gap by analyzing multiple popular on three tasks. To best our knowledge, are first utilize new Medicare insurance cl...

Most traditional supervised classification learning algorithms are ineffective for highly imbalanced time series classification, which has received considerably less attention than imbalanced data problems in data mining and machine learning research. Bagging is one of the most effective ensemble learning methods, yet it has drawbacks on highly imbalanced data. Sampling methods are considered t...

In this paper, we are exploring the response of individual classifier families on imbalanced medical data. In this work we are using LIDC (Lung Image Database Consortium) dataset, which is a very good example for imbalanced data. The main objective of this work is to examine how will be the response of different categories of classifier on imbalanced dataset. We are considering five categories ...

Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Clas...

Clinical datasets commonly have an imbalanced class distribution and high dimensional variables. Imbalanced class means that one class is represented by a large number (majority) of samples more than another (minority) one in binary classification [1]. For example, in our research dataset there are 1459 instances classified as “Alive” while 485 are classified as “Dead”. Machine learning is gene...

The data imbalance problem is a frequent bottleneck in the classification performance of neural networks. In this paper, we propose novel supervised discriminative feature generation (DFG) method for minority class dataset. DFG based on modified structure generative adversarial network consisting four independent networks: generator, discriminator, extractor, and classifier. To augment selected...

Abstract Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still difficulty generalizing to minority classes and their decisions are difficult interpret. These problems related because the method by which CNNs generalize classes, requires improvement, is wrapped in a black-box. To demystify CNN we focus latent features. Although embed patte...

As a new and efficient ensemble learning algorithm, XGBoost has been widely applied for its multitudinous advantages, but classification effect in the case of data imbalance is often not ideal. Aiming at this problem, an attempt was made to optimize regularization term XGBoost, algorithm based on mixed sampling proposed. The main idea combine SVM-SMOTE over-sampling EasyEnsemble under-sampling ...

Data in the health sector are often lacking and unbalanced. It is because collecting data takes time many resources. One example sleep apnea which about 8–10 hours to get uses specialized hardware like polysomnography (PSG). This study proposes a augmentation technique handle unbalanced using DCGAN several deep learning models such as 1D-CNN, ANN, LSTM, 1D-CNN+LSTM classifier for detection. The...