Luis Torgo

Latest

• Evaluation procedures for forecasting with spatiotemporal data
• SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels
• Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
• Evaluating time series forecasting models: An empirical study on performance estimation methods
• A review on web content popularity prediction: Issues and open challenges
• Evaluating time series forecasting models: An empirical study on performance estimation methods
• Layered Learning for Early Anomaly Detection: Predicting Critical Health Episodes
• Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters
• On Feature Selection and Evaluation of Transportation Mode Prediction Strategies
• Pre-processing approaches for imbalanced distributions in regression
• The CURE for Class Imbalance
• Visual Interpretation of Regression Error
• Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles
• Contributions to the Detection of Unreliable Twitter Accounts through Analysis of Content and Behaviour
• Cost-Sensitive Learning: Preface
• Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges
• Data Mining with R: Learning with Case Studies, Second Edition (chinese edition)
• Evaluation procedures for forecasting with spatio-temporal data
• Multi-Source Social Feedback of Online News Feeds
• REBAGG: REsampled BAGGing for Imbalanced Regression
• Resampling with neighbourhood bias on imbalanced domains
• The Utility Problem of Web Content Popularity Prediction
• Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons
• A Comparative Study of Performance Estimation Methods for Time Series Forecasting
• A Framework for Recommendation of Highly Popular News Lacking Social Feedback
• Arbitrated Ensemble for Solar Radiation Forecasting
• Arbitrated Ensemble for Time Series Forecasting
• Data Mining with R: Learning with Case Studies, Second Edition
• Dynamic and Heterogeneous Ensembles for Time Series Forecasting
• Evaluation of Ensemble Methods in Imbalanced Regression Tasks
• Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems
• Learning Through Utility Optimization in Regression Tasks
• Learning with Imbalanced Domains: preface
• Relevance-based Evaluation Metrics for Multi-class Imbalanced Domains
• SMOGN: a Pre-processing Approach for Imbalanced Regression
• A comparative study of approaches to forecast the correct trading actions
• A UBL: an R package for Utility-based Learning
• Data-driven relevance judgments for ranking evaluation
• Model Trees
• Predicting Wildfires: Propositional and Relational Spatio-Temporal Pre-Processing Approaches
• Regression Trees
• Resampling Strategies for Imbalanced Time Series
• Time-Based Ensembles for Prediction of Rare Events in News Stream
• A Survey of Predictive Modelling under Imbalanced Distributions
• Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?
• Forecasting the Correct Trading Actions
• Re-sampling Strategies for Regression
• Socially Driven News Recommendation
• An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R
• Ensembles for Time Series Forecasting
• Improvement of News Ranking through Importance Prediction
• Resampling approaches to improve news importance prediction
• A RapidMiner extension for Open Machine Learning
• OpenML: A collaborative Science Platform
• OpenML: networked science in machine learning
• SMOTE for Regression
• Classifying News Stories with a Constrained Learning Strategy to Estimate the Direction of a Market Index
• Data Mining with R: Learning with Case Studies (Chinese Edition)
• Wind speed forecasting using spatio-temporal indicators
• 2D-interval predictions for time series
• A Contextual Classification Strategy for Polarity Analysis of Direct Quotations from Financial News
• Guided Self Training for Sentiment Classification
• Model Trees
• Regression Trees
• Utility-Based Fraud Detection
• Data Mining with R: Learning with Case Studies
• Interval Forecast of Water Quality Parameters
• Modelos de Previsao de Valores Extremos e Raros (in Portuguese)
• Predictive models for forecasting hourly urban water demand
• Resource-bounded Outlier Detection Using Clustering Methods
• A Linguagem R, programação para a análise de dados
• Detecting Errors in Foreign Trade Transactions: Dealing with Insufficient Data
• Deteção de fraude usando o R: um caso de estudo
• Precision and Recall for Regression.
• A Comparative Study on Predicting Algae Blooms in Douro River, Portugal
• Utility-based performance measures for regression
• Resource-bounded Fraud Detection
• Utility-based Regression
• Automatic Selection of Table Areas in Documents for Information Extraction.
• Predicting Harmful Algae Blooms.
• Thesis: Inductive learning to tree-based regression models.
• Dynamic Discretization of Continuous Attributes.
• Regression by Classification
• Applying Propositional Learning to Time Series Prediction
• Data Fitting with Rule-based Regression
• Controlled Redundancy in incremental Rule Learning
• Rule Combination in Inductive Learning
• Knowledge Acquisition via Knowledge Integration