Search Results for author:
Found 17 papers, 12 papers with code
Multi-output Ensembles for Multi-step Forecasting
On the other hand, the literature regarding the application of dynamic ensembles for multi-step ahead forecasting is scarce.
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
We frame the task of predicting extreme values of significant wave height as an exceedance probability forecasting problem.
Automated Imbalanced Classification via Layered Learning
The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations.
Beyond Average Performance -- exploring regions of deviating performance for black box classification models
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.
Model Compression for Dynamic Forecast Combination
In this paper, we leverage the idea of model compression to address this problem in time series forecasting tasks.
Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators
We address this issue and compare a set of estimation methods for model selection in time series forecasting tasks.
A Survey on Spatio-temporal Data Analytics Systems
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.
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection
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.
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
The early detection of anomalous events in time series data is essential in many domains of application.
Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation
One of the most critical steps for trajectory data mining is segmentation.
Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters
Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows.
Evaluating time series forecasting models: An empirical study on performance estimation methods
In this paper we address the application of these methods to time series forecasting tasks.
How to evaluate sentiment classifiers for Twitter time-ordered data?
Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data.
UBL: an R package for Utility-based Learning
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.
A Survey of Predictive Modelling under Imbalanced Distributions
Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable.
An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R
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.
OpenML: networked science in machine learning
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.
