Several applications aim to identify rare events from very large data sets. Classification algorithms may present great limitations on large data sets and show a performance degradation due to class imbalance. Many solutions have been presented in literature to deal with the problem of huge amount of data or imbalancing separately. In this paper we assessed the performances of a novel method, P...

Dermoskopi görüntüleme, deri kanseri teşhisi için dermotolojistler tarafından yaygın bir biçimde kullanılan tanı metodudur. Dermotolojik değerlendirmenin uzman kişiye bağlı, zaman alıcı ve sübjektif olmasından dolayı otomatik sistemler karar verme süreçlerine katkı sağlamaları tercih edilmektedir. Deri lezyon görüntülerinden melanomların tespit edilmesi hastalığın erken ile tedavi sürecini hızl...

The class imbalance problem in two-class data sets is one of the most important problems. When examples of one class in a training data set vastly outnumber examples of the other class, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class. There are several algorithms to alleviate the problem of class imbalance in literature. In this pa...

Landslides are well-known phenomena that cause significant changes to the relief of an area’s terrain, often causing damage technical infrastructure and loss life. One possible means reducing negative impact landslides on people’s lives or property is recognize areas prone their occurrence. The most common approach this problem preparing landslide susceptibility maps. These can factor in actual...

The acquisition of face images is usually limited due to policy and economy considerations, and hence the number of training examples of each subject varies greatly. The problem of face recognition with imbalanced training data has drawn attention of researchers and it is desirable to understand in what circumstances imbalanced data set affects the learning outcomes, and robust methods are need...

Abstract The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A‐Share listed firms. Based on a sample of 33,544 Chinese firm‐year instances from 1998 2017, this respectively established one logistic regression and four classifiers (AdaBoost, XGBoost, CUSBoost, RUSBoost) by 12 financial ratios 28 raw data. Additio...