| Assessing Batch and Online Learning for Delivery in Full and On Time Predictions | 0 | 0.34 | 2022 |
| Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition | 0 | 0.34 | 2022 |
| Classifying Hierarchical Data Streams using Global Classifiers and Summarization Techniques | 0 | 0.34 | 2022 |
| Adaptive Global k-Nearest Neighbors for Hierarchical Classification of Data Streams * | 0 | 0.34 | 2021 |
| Dynamically Selected Ensemble for Data Stream Classification | 0 | 0.34 | 2021 |
| Interactive Process Drift Detection Framework. | 0 | 0.34 | 2021 |
| A Case Study Of Batch And Incremental Recommender Systems In Supermarket Data Under Concept Drifts And Cold Start | 0 | 0.34 | 2021 |
| Classifying Potentially Unbounded Hierarchical Data Streams with Incremental Gaussian Naive Bayes. | 0 | 0.34 | 2021 |
| UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering | 0 | 0.34 | 2021 |
| Towards the Overcome of Performance Pitfalls in Data Stream Mining Tools | 0 | 0.34 | 2021 |
| Naïve Approaches to Deal With Concept Drifts. | 0 | 0.34 | 2020 |
| Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms. | 0 | 0.34 | 2020 |
| ADADRIFT - An Adaptive Learning Technique for Long-history Stream-based Recommender Systems. | 0 | 0.34 | 2020 |
| Lessons learned from data stream classification applied to credit scoring | 0 | 0.34 | 2020 |
| Combining Slow and Fast Learning for Improved Credit Scoring. | 0 | 0.34 | 2020 |
| Cost-sensitive learning for imbalanced data streams | 0 | 0.34 | 2020 |
| Regularized And Incremental Decision Trees For Data Streams | 0 | 0.34 | 2020 |
| Machine learning for streaming data: state of the art, challenges, and opportunities | 10 | 0.50 | 2019 |
| Vertical and Horizontal Partitioning in Data Stream Regression Ensembles | 0 | 0.34 | 2019 |
| Decision tree-based feature ranking in concept drifting data streams. | 0 | 0.34 | 2019 |
| Boosting decision stumps for dynamic feature selection on data streams. | 4 | 0.44 | 2019 |
| Learning regularized hoeffding trees from data streams. | 0 | 0.34 | 2019 |
| Merit-guided dynamic feature selection filter for data streams. | 4 | 0.41 | 2019 |
| Correction to: Adaptive random forests for evolving data stream classification | 0 | 0.34 | 2019 |
| Are fintechs really a hype? A machine learning-based polarity analysis of Brazilian posts on social media | 0 | 0.34 | 2018 |
| Adaptive random forests for data stream regression. | 0 | 0.34 | 2018 |
| Iterative subset selection for feature drifting data streams. | 2 | 0.39 | 2018 |
| A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. | 0 | 0.34 | 2017 |
| Adaptive random forests for evolving data stream classification. | 22 | 0.70 | 2017 |
| A Survey on Ensemble Learning for Data Stream Classification. | 51 | 1.29 | 2017 |
| Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques. | 2 | 0.42 | 2017 |
| SNCStream+: Extending a high quality true anytime data stream clustering algorithm. | 4 | 0.39 | 2016 |
| On Dynamic Feature Weighting for Feature Drifting Data Streams. | 5 | 0.42 | 2016 |
| On the Discovery of Time Distance Constrained Temporal Association Rules | 0 | 0.34 | 2015 |
| Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory | 1 | 0.36 | 2015 |
| Pairwise combination of classifiers for ensemble learning on data streams | 2 | 0.39 | 2015 |
| Analyzing the Impact of Feature Drifts in Streaming Learning. | 3 | 0.38 | 2015 |
| SNCStream: a social network-based data stream clustering algorithm | 5 | 0.41 | 2015 |
| Applying Ensemble-based Online Learning Techniques on Crime Forecasting. | 0 | 0.34 | 2015 |
| A Survey on Feature Drift Adaptation | 18 | 0.66 | 2015 |
| A Complex Network-Based Anytime Data Stream Clustering Algorithm. | 1 | 0.36 | 2015 |
| SFNClassifier: a scale-free social network method to handle concept drift | 6 | 0.45 | 2014 |
