Predicting breast cancer recurrence using machine learning techniques: a systematic review
Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically
related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast …
related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast …
Cost-sensitive learning of deep feature representations from imbalanced data
Class imbalance is a common problem in the case of real-world object detection and
classification tasks. Data of some classes are abundant, making them an overrepresented …
classification tasks. Data of some classes are abundant, making them an overrepresented …
Prediction of weather-induced airline delays based on machine learning algorithms
The primary goal of the model proposed in this paper is to predict airline delays caused by
inclement weather conditions using data mining and supervised machine learning …
inclement weather conditions using data mining and supervised machine learning …
Review of random forest classification techniques to resolve data imbalance
In this current age, numerous ranges of real word applications with imbalanced dataset is
one of the foremost focal point of researcher's inattention. There is the enormous increment …
one of the foremost focal point of researcher's inattention. There is the enormous increment …
Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise
Class imbalance learning is a recent topic, which helps us to detect the classes from
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
Theory-guided machine learning in materials science
N Wagner, JM Rondinelli - Frontiers in Materials, 2016 - frontiersin.org
Materials scientists are increasingly adopting the use of machine learning tools to discover
hidden trends in data and make predictions. Applying concepts from data science without …
hidden trends in data and make predictions. Applying concepts from data science without …
[HTML][HTML] Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction
C Sanchis-Segura, MV Ibañez-Gual, N Aguirre… - Scientific Reports, 2020 - nature.com
Sex differences in 116 local gray matter volumes (GM VOL) were assessed in 444 males
and 444 females without correcting for total intracranial volume (TIV) or after adjusting the …
and 444 females without correcting for total intracranial volume (TIV) or after adjusting the …
ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data
In DNA microarray data, class imbalance problem occurs frequently, causing poor prediction
performance for minority classes. Moreover, its other features, such as high-dimension …
performance for minority classes. Moreover, its other features, such as high-dimension …
Seismic fault detection using convolutional neural networks with focal loss
Fault detection is a fundamental and important research topic in automatic seismic
interpretation since the geometry of faults usually reveals the accumulation and migration of …
interpretation since the geometry of faults usually reveals the accumulation and migration of …
Telecommunication subscribers' churn prediction model using machine learning
SA Qureshi, AS Rehman, AM Qamar… - … conference on digital …, 2013 - ieeexplore.ieee.org
During the last two decades, we have seen mobile communication becoming the dominant
medium of communication. In numerous countries, especially the developed ones, the …
medium of communication. In numerous countries, especially the developed ones, the …