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...

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is appli...

The paper discusses problems of constructing classifiers from imbalanced data. Re-sampling approaches that change the original class distribution are often used to improve performance of classifiers for the minority class. We describe a new approach to selective pre-processing of imbalanced data which combines local over-sampling of the minority class with filtering difficult examples from majo...

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...

One of the main objectives of a Machine Learning – ML – system is to induce a classifier that minimizes classification errors. Two relevant topics in ML are the understanding of which domain characteristics and inducer limitations might cause an increase in misclassification. In this sense, this work analyzes two important issues that might influence the performance of ML systems: class imbalan...

Research aims: This study aims to create an early detection model predict events in the Indonesian capital market.Design/Methodology/Approach: A quantitative comparing ensemble learning models with imbalanced data handling detected market events. used five models—Random Forest, ExtraTrees, CatBoost, XGBoost, and LightGBM—to detect by data, such as under sampling (RUS), oversampling (SMOTE, SMOT...

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling into SMOTE over-sampling technique, working with several classification algorithms from machine learning field to integrate binding site predictions. In this paper, we improve the classification result with the aid of Tomek ...