A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more i...

In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a nove...

Imbalanced learning data often emerges during the process of the knowledge discovery in data and presents a significant challenge for data mining methods. In this paper we investigate the influence of class imbalanced data on: artificial intelligence methods i.e. neural networks and support vector machine and on classical classification methods represented by RIPPER and Naïve Bayes classifier. ...

Imbalanced data classification is a common issue in mining where the classifiers are skewed towards larger class. Classification of high-dimensional (imbalanced) great interest to decision-makers as it more difficult to. The dimension reduction method, process which variables reduced, allows high dimensional datasets be interpreted easily with certain loss. This study, method combining SMOTE ov...

In December 2022, the development of a franchise for an ice cream and tea outlet from China named Mixue became talk Indonesian people, especially on social media Twitter, giving rise to various opinions public regarding which is growing so rapidly. So that, sentiment analysis will be carried out by classifying using implementation Support Vector Machine (SVM) algorithm. From results research th...

Abstract The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample makes fault diagnosis and accuracy rate low, capability to classify minority-class samples is restricted. To address that algorithm machine learning insufficient identify minority class for problems. Therefore, this paper proposes an improved support vector (SVM) method...

Aiming at the imbalance problem of wireless link samples, we propose quality estimation method which combines K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The adopts mean, variance asymmetry metrics physical layer parameters as parameters. is measured by level determined packet receiving rate. used to cluster samples. SMOTE employed synthesize s...

In class-imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to tackle problems from the data level, whereas SMOTE blindly selects neighboring minority class points when performing an interpolation among them and inevitably brings collinearity between generated new original ones. To combat these problems, we propose in this study adaptive-weighting m...

With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...