Concept drifting is always an interesting problem. For instance, a user is interested in a set of topics, X, for a period, may switches to a different set of topics, Y, in the next period. In this paper, we focus on two issues of concept drifts, namely, concept drifts detection and model adaptation in a text stream context. We use statistical control to detect concept drifts, and propose a new multi-classifier strategy for model adaptation. We conducted extensive experiments and reported our findings in this paper
Data classification in streams where the underlying distribution changes over time is known to be di...
In many real-world classification problems the concept being modelled is not static but rather chang...
Concept drift primarily refers to an online supervised learning scenario when the relation between t...
Usually concept drift occurs in many applications of machine learning. Detecting a concept drift is ...
Concept drift in data streams can cause significant performance degradation of existing classificati...
The detection of concept drift allows to point out when a data stream changes its behavior over time...
Data collected over time often exhibit changes in distribution, or concept drift, caused by changes ...
Abstract. Concept drift is a common phenomenon in streaming data environments and constitutes an int...
Machine learning applications in streaming data often grapple with dynamic changes in data distribut...
The task of information filtering is to classify texts from a stream of documents into relevant and ...
In the data stream classification process, in addition to the solution of massive and real-time data...
. The task of information filtering is to classify texts from a stream of documents into relevant an...
Abstract. This paper addresses the task of learning concept descriptions from streams of data. As ne...
This paper addresses the task of learning concept descriptions from streams of data. As new data are...
Abstract.Classifying streaming data requires the development of methods which are com-putationally e...
Data classification in streams where the underlying distribution changes over time is known to be di...
In many real-world classification problems the concept being modelled is not static but rather chang...
Concept drift primarily refers to an online supervised learning scenario when the relation between t...
Usually concept drift occurs in many applications of machine learning. Detecting a concept drift is ...
Concept drift in data streams can cause significant performance degradation of existing classificati...
The detection of concept drift allows to point out when a data stream changes its behavior over time...
Data collected over time often exhibit changes in distribution, or concept drift, caused by changes ...
Abstract. Concept drift is a common phenomenon in streaming data environments and constitutes an int...
Machine learning applications in streaming data often grapple with dynamic changes in data distribut...
The task of information filtering is to classify texts from a stream of documents into relevant and ...
In the data stream classification process, in addition to the solution of massive and real-time data...
. The task of information filtering is to classify texts from a stream of documents into relevant an...
Abstract. This paper addresses the task of learning concept descriptions from streams of data. As ne...
This paper addresses the task of learning concept descriptions from streams of data. As new data are...
Abstract.Classifying streaming data requires the development of methods which are com-putationally e...
Data classification in streams where the underlying distribution changes over time is known to be di...
In many real-world classification problems the concept being modelled is not static but rather chang...
Concept drift primarily refers to an online supervised learning scenario when the relation between t...