SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
Dynamic classifier selection: Recent advances and perspectives
Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
Ovanet: One-vs-all network for universal domain adaptation
Abstract Universal Domain Adaptation (UNDA) aims to handle both domain-shift and
category-shift between two datasets, where the main challenge is to transfer knowledge …
category-shift between two datasets, where the main challenge is to transfer knowledge …
[HTML][HTML] Multi-class sentiment classification on Bengali social media comments using machine learning
Abstract Multi-class Sentiment Analysis (SA) is an important field of computational linguistics
that extracts multiple opinions expressed in a text using NLP and text-mining techniques …
that extracts multiple opinions expressed in a text using NLP and text-mining techniques …
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
P Vuttipittayamongkol, E Elyan - Information Sciences, 2020 - Elsevier
Class imbalanced datasets are common across different domains including health, security,
banking and others. A typical supervised learning algorithm tends to be biased towards the …
banking and others. A typical supervised learning algorithm tends to be biased towards the …
RSMOTE: A self-adaptive robust SMOTE for imbalanced problems with label noise
Imbalanced classification is an important task in supervised learning, and Synthetic Minority
Over-sampling Technique (SMOTE) is the most common method to address it. However, the …
Over-sampling Technique (SMOTE) is the most common method to address it. However, the …
Imminent loss of climate space for permafrost peatlands in Europe and Western Siberia
Human-induced climate warming by 2100 is expected to thaw large expanses of northern
permafrost peatlands. However, the spatio-temporal dynamics of permafrost peatland thaw …
permafrost peatlands. However, the spatio-temporal dynamics of permafrost peatland thaw …