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20152022
most citedA Survey of Predictive Modelling under Imbalanced Distributions

173 citations · 278 across the 8 of their papers we have counts for

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cs.LG2022

Automated Imbalanced Classification via Layered Learning

Vitor Cerqueira, Luis Torgo, Paula Branco +1

In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach t…

cs.LG20211 cited

Beyond Average Performance -- exploring regions of deviating performance for black box classification models

Luis Torgo, Paulo Azevedo, Ines Areosa

Machine learning models are becoming increasingly popular in different types of settings. This is mainly caused by their ability to achieve a level of predictive performance that i…

cs.LG2021

A Survey on Spatio-temporal Data Analytics Systems

Md Mahbub Alam, Luis Torgo, Albert Bifet

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to…

cs.LG2021

STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection

Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet +1

Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive m…

cs.LG2020

Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation

Mohammad Etemad, Zahra Etemad, Amilcar Soares +3

Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for…

cs.LG2015173 cited

A Survey of Predictive Modelling under Imbalanced Distributions

Paula Branco, Luis Torgo, Rita Ribeiro

Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least co…