1 code implementation • 26 Jun 2023 • Vitor Cerqueira, Luis Torgo
On the other hand, the literature regarding the application of dynamic ensembles for multi-step ahead forecasting is scarce.
no code implementations • 20 Jun 2022 • Vitor Cerqueira, Luis Torgo
This task is framed as an exceedance probability forecasting problem.
no code implementations • 5 May 2022 • Vitor Cerqueira, Luis Torgo, Paula Branco, Colin Bellinger
The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations.
no code implementations • 16 Sep 2021 • Luis Torgo, Paulo Azevedo, Ines Areosa
In this paper we describe two general approaches that can be used to provide interpretable descriptions of the expected performance of any black box classification model.
1 code implementation • 5 Apr 2021 • Vitor Cerqueira, Luis Torgo, Carlos Soares, Albert Bifet
In this paper, we leverage the idea of model compression to address this problem in time series forecasting tasks.
1 code implementation • 1 Apr 2021 • Vitor Cerqueira, Luis Torgo, Carlos Soares
We address this issue and compare a set of estimation methods for model selection in time series forecasting tasks.
1 code implementation • 17 Mar 2021 • Md Mahbub Alam, Luis Torgo, Albert Bifet
Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems.
1 code implementation • 1 Mar 2021 • Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet, Luis Torgo
In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
1 code implementation • 22 Oct 2020 • Vitor Cerqueira, Luis Torgo, Carlos Soares
The early detection of anomalous events in time series data is essential in many domains of application.
1 code implementation • 23 Mar 2020 • Mohammad Etemad, Zahra Etemad, Amilcar Soares, Vania Bogorny, Stan Matwin, Luis Torgo
One of the most critical steps for trajectory data mining is segmentation.
1 code implementation • 29 Sep 2019 • Vitor Cerqueira, Luis Torgo, Carlos Soares
Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows.
1 code implementation • 28 May 2019 • Vitor Cerqueira, Luis Torgo, Igor Mozetic
In this paper we address the application of these methods to time series forecasting tasks.
no code implementations • 14 Mar 2018 • Igor Mozetič, Luis Torgo, Vitor Cerqueira, Jasmina Smailović
Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data.
no code implementations • 27 Apr 2016 • Paula Branco, Rita P. Ribeiro, Luis Torgo
The main goal of UBL package is to facilitate the utility-based predictive analytic task by providing a set of methods to deal with this type of problems in the R environment.
1 code implementation • 7 May 2015 • 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.
1 code implementation • 1 Dec 2014 • Luis Torgo
The overall goal of the infra-structure provided by our package is to facilitate the task of estimating the predictive performance of different modeling approaches to predictive tasks in the R environment.
1 code implementation • 29 Jul 2014 • Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luis Torgo
Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals.