7 citations · 15 across the 10 of their papers we have counts for
5 papers · 1 filter
iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework
Asif Newaz, Asif Ur Rahman Adib, Taskeed Jabid
Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class. Cost-sensitive learnin…
iBRF: Improved Balanced Random Forest Classifier
Asif Newaz, Md. Salman Mohosheu, MD. Abdullah al Noman +1
Class imbalance poses a major challenge in different classification tasks, which is a frequently occurring scenario in many real-world applications. Data resampling is considered t…
An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer
Asif Newaz, Abdullah Taharat, Md Sakibul Islam +1
Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women…
An Empirical Analysis of the Efficacy of Different Sampling Techniques for Imbalanced Classification
Asif Newaz, Shahriar Hassan, Farhan Shahriyar Haq
Learning from imbalanced data is a challenging task. Standard classification algorithms tend to perform poorly when trained on imbalanced data. Some special strategies need to be a…
A Novel Hybrid Sampling Framework for Imbalanced Learning
Asif Newaz, Farhan Shahriyar Haq
Class imbalance is a frequently occurring scenario in classification tasks. Learning from imbalanced data poses a major challenge, which has instigated a lot of research in this ar…