A comprehensive survey on rare event prediction
Rare event prediction involves identifying and forecasting events with a low probability using
machine learning (ML) and data analysis. Due to the imbalanced data distributions, where …
machine learning (ML) and data analysis. Due to the imbalanced data distributions, where …
WOA+ BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network
Nowadays, big data plays a substantial part in information knowledge analysis,
manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets …
manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets …
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection …
SMH Mahmud, W Chen, Y Liu, MA Awal… - Briefings in …, 2021 - academic.oup.com
Discovering drug–target (protein) interactions (DTIs) is of great significance for researching
and developing novel drugs, having a tremendous advantage to pharmaceutical industries …
and developing novel drugs, having a tremendous advantage to pharmaceutical industries …
A Comprehensive Survey on Rare Event Prediction
CS Jayakody Kankanamalage… - ACM Computing …, 2024 - scholarcommons.sc.edu
Rare event prediction involves identifying and forecasting events with a low probability using
machine learning (ML) and data analysis. Due to the imbalanced data distributions, where …
machine learning (ML) and data analysis. Due to the imbalanced data distributions, where …
[HTML][HTML] Evaluating the role of data enrichment approaches towards rare event analysis in manufacturing
Rare events are occurrences that take place with a significantly lower frequency than more
common, regular events. These events can be categorized into distinct categories, from …
common, regular events. These events can be categorized into distinct categories, from …
Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques
Drug discovery relies on predicting drug-target interaction (DTI), which is an important
challenging task. The purpose of DTI is to identify the interaction between drug chemical …
challenging task. The purpose of DTI is to identify the interaction between drug chemical …
MMA: metadata supported multi-variate attention for onset detection and prediction
Deep learning has been applied successfully in sequence understanding and translation
problems, especially in univariate, unimodal contexts, where large number of supervision …
problems, especially in univariate, unimodal contexts, where large number of supervision …
KNN-based overlapping samples filter approach for classification of imbalanced data
MM Nwe, KT Lynn - Software Engineering Research, Management and …, 2020 - Springer
Imbalanced data classification is one of the most interesting problems in various real-world
data sets. The class distribution of imbalanced data set strongly affects the classification rate …
data sets. The class distribution of imbalanced data set strongly affects the classification rate …
Optimal Downsampling for Imbalanced Classification with Generalized Linear Models
Y Chen, J Blanchet, K Dembczynski, LF Nern… - arXiv preprint arXiv …, 2024 - arxiv.org
Downsampling or under-sampling is a technique that is utilized in the context of large and
highly imbalanced classification models. We study optimal downsampling for imbalanced …
highly imbalanced classification models. We study optimal downsampling for imbalanced …
Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier
Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood
serum. It has a damaging impact on several noble body systems. Today, the concept of …
serum. It has a damaging impact on several noble body systems. Today, the concept of …