Add to ORKGAbstract. A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is its adaptability in presence of concept drift. Changes in data can cause serious deterioration of the ensemble performance. Our approach is able to discover changes by adopting a strategy based on self-similarity of the ensemble behavior, measured by its fractal dimension, and to revise itself by promptly restoring classification accuracy. Experimental results on a synthetic data set show the validity...
In order to improve the performance of online learning in the real-time distribution of streaming da...
Data mining (DM) is the process of finding patterns and relationships in databases.The breakthrough ...
Ensemble learning is a commonly used tool for building prediction models from data streams, due to i...
The extensive growth of digital technologies such as the Internet of Things (IoT), social media netw...
The extensive growth of digital technologies has led to new challenges in terms of processing and di...
Among the many issues related to data stream applications, those involved in predictive tasks such a...
In this study, we introduce a novel framework for non-stationary data stream classification problems...
This paper presents a novel ensemble learning method based on evolutionary algorithms to cope with d...
The requirements of Machine Learning applications are changing rapidly. Machine Learning models need...
Data stream classification is the process of learning supervised models from continuous labelled exa...
Data stream classification techniques have been playing an important role in big data analytics rece...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data rec...
AbstractThe problem addressed in this paper concerns mining data streams with concept drift. The goa...
© 2017, The Author(s). A novel online ensemble strategy, ensemble BPegasos (EBPegasos), is proposed ...
In order to improve the performance of online learning in the real-time distribution of streaming da...
Data mining (DM) is the process of finding patterns and relationships in databases.The breakthrough ...
Ensemble learning is a commonly used tool for building prediction models from data streams, due to i...
The extensive growth of digital technologies such as the Internet of Things (IoT), social media netw...
The extensive growth of digital technologies has led to new challenges in terms of processing and di...
Among the many issues related to data stream applications, those involved in predictive tasks such a...
In this study, we introduce a novel framework for non-stationary data stream classification problems...
This paper presents a novel ensemble learning method based on evolutionary algorithms to cope with d...
The requirements of Machine Learning applications are changing rapidly. Machine Learning models need...
Data stream classification is the process of learning supervised models from continuous labelled exa...
Data stream classification techniques have been playing an important role in big data analytics rece...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data rec...
AbstractThe problem addressed in this paper concerns mining data streams with concept drift. The goa...
© 2017, The Author(s). A novel online ensemble strategy, ensemble BPegasos (EBPegasos), is proposed ...
In order to improve the performance of online learning in the real-time distribution of streaming da...
Data mining (DM) is the process of finding patterns and relationships in databases.The breakthrough ...
Ensemble learning is a commonly used tool for building prediction models from data streams, due to i...