Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning
imbalanced-learn is an open-source python toolbox aiming at providing a wide range of
methods to cope with the problem of imbalanced dataset frequently encountered in machine …
methods to cope with the problem of imbalanced dataset frequently encountered in machine …
A machine learning method for classification of cervical cancer
Cervical cancer is one of the leading causes of premature mortality among women
worldwide and more than 85% of these deaths are in develo** countries. There are …
worldwide and more than 85% of these deaths are in develo** countries. There are …
Data sampling methods to deal with the big data multi-class imbalance problem
The class imbalance problem has been a hot topic in the machine learning community in
recent years. Nowadays, in the time of big data and deep learning, this problem remains in …
recent years. Nowadays, in the time of big data and deep learning, this problem remains in …
Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy
H de Medeiros Back, ECV Junior, OE Alarcon… - Chemosphere, 2022 - Elsevier
Microplastics are contaminants of emerging concern-not only environmental, but also to
human health. Characterizing them is of fundamental importance to evaluate their potential …
human health. Characterizing them is of fundamental importance to evaluate their potential …
Measuring data
We identify the task of measuring data to quantitatively characterize the composition of
machine learning data and datasets. Similar to an object's height, width, and volume, data …
machine learning data and datasets. Similar to an object's height, width, and volume, data …
Uncertainty based under-sampling for learning naive bayes classifiers under imbalanced data sets
In many real world classification tasks, all data classes are not represented equally. This
problem, known also as the curse of class imbalanced in data sets, has a potential impact in …
problem, known also as the curse of class imbalanced in data sets, has a potential impact in …
The proposal of undersampling method for learning from imbalanced datasets
Highly imbalanced data, which occurs in many real-world applications, often makes
machine-based processing difficult or even impossible. The over-and under-sampling …
machine-based processing difficult or even impossible. The over-and under-sampling …
A survey of machine learning methods and challenges for windows malware classification
Malware classification is a difficult problem, to which machine learning methods have been
applied for decades. Yet progress has often been slow, in part due to a number of unique …
applied for decades. Yet progress has often been slow, in part due to a number of unique …
Combining over-sampling and under-sampling techniques for imbalance dataset
N Junsomboon, T Phienthrakul - … of the 9th international conference on …, 2017 - dl.acm.org
An important problem in medical data analysis is imbalance dataset. This problem is a
cause of diagnostic mistake. The results of diagnostic affect to life of patients. If a doctor fails …
cause of diagnostic mistake. The results of diagnostic affect to life of patients. If a doctor fails …
[HTML][HTML] Type 2 diabetes mellitus screening and risk factors using decision tree: results of data mining
Objectives: The aim of this study was to examine a predictive model using features related to
the diabetes type 2 risk factors. Methods: The data were obtained from a database in a …
the diabetes type 2 risk factors. Methods: The data were obtained from a database in a …