This master's thesis deals with knowledge discovery and is focused on data stream classification. Three ensemble classification methods are described here. These methods are implemented in practical part of this thesis and are included in the classification system. Extensive measurements and experimentation were used for method analysis and comparison. Implemented methods were then integrated into Malware analysis system. At the conclusion are presented obtained results
AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDS...
The paper presents an ensemble classification method based on clustering, along with its implementat...
In this paper, we study the problem of learning from concept drifting data streams with noise, where...
Data streaming is the transmission of a continuous data stream which is often fed into stream proces...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Antimalware offers detection mechanism to detect and take appropriate action against malware detecte...
Data stream classification is the process of learning supervised models from continuous labelled exa...
The aims of this paper were to provide a comprehensive review of classification techniques and their...
In many applications of information systems learning algorithms have to act in dynamic environments ...
In machine learning and statistics, ensemble methods employ multiple models to obtain better perform...
A new ensemble algorithm on data stream classification using recurring concepts detectio
Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enabl...
The data stream model for data mining places harsh restrictions on a learning algorithm. A model mus...
AbstractThe problem addressed in this paper concerns mining data streams with concept drift. The goa...
Among the many issues related to data stream applications, those involved in predictive tasks such a...
AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDS...
The paper presents an ensemble classification method based on clustering, along with its implementat...
In this paper, we study the problem of learning from concept drifting data streams with noise, where...
Data streaming is the transmission of a continuous data stream which is often fed into stream proces...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Antimalware offers detection mechanism to detect and take appropriate action against malware detecte...
Data stream classification is the process of learning supervised models from continuous labelled exa...
The aims of this paper were to provide a comprehensive review of classification techniques and their...
In many applications of information systems learning algorithms have to act in dynamic environments ...
In machine learning and statistics, ensemble methods employ multiple models to obtain better perform...
A new ensemble algorithm on data stream classification using recurring concepts detectio
Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enabl...
The data stream model for data mining places harsh restrictions on a learning algorithm. A model mus...
AbstractThe problem addressed in this paper concerns mining data streams with concept drift. The goa...
Among the many issues related to data stream applications, those involved in predictive tasks such a...
AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDS...
The paper presents an ensemble classification method based on clustering, along with its implementat...
In this paper, we study the problem of learning from concept drifting data streams with noise, where...