Networks of classifiers can offer improved accuracy and scalability over single classifiers by utilizing distributed processing resources and analytics. However, they also pose a unique combination of challenges. First, classifiers may be located across different sites that are willing to cooperate to provide services, but are unwilling to reveal proprietary information about their analytics, or are unable to exchange their analytics due to the high transmission overheads involved. Furthermore, processing of voluminous stream data across sites often requires load shedding approaches, which can lead to suboptimal classification performance. Finally, real stream mining systems often exhibit dynamic behavior and thus necessitate frequent recon...
Advances in hardware and software in the past decade allow to capture, record and process fast data ...
Modern society produces vast streams of data. Many stream mining algorithms have been developed to c...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
We consider the problem of optimally configuring classifier chains for real-time multimedia stream m...
We consider the problem of configuring classifier trees in dis-tributed stream mining system. The co...
AbstractData-driven, adaptive computations are key to enabling the deployment of accurate and effici...
We consider the problem of resource allocation in mining multiple data streams. Due to the large vol...
Mining data streams has recently become an important and challenging task for a wide range of applic...
Recently, mining from data streams has become an important and challenging task for many real-world ...
Recently, mining from data streams has become an important and challenging task for many real-world ...
Abstract—Emerging stream mining applications require clas-sification of large data streams generated...
In this paper, we propose a distributed solution to the prob-lem of configuring classifier trees in ...
Data streams have become ubiquitous in recent years and are handled on a variety of platforms, rangi...
Data mining (DM) is the process of finding patterns and relationships in databases.The breakthrough ...
Two critical challenges typically associated with mining data streams are concept drift and data con...
Advances in hardware and software in the past decade allow to capture, record and process fast data ...
Modern society produces vast streams of data. Many stream mining algorithms have been developed to c...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
We consider the problem of optimally configuring classifier chains for real-time multimedia stream m...
We consider the problem of configuring classifier trees in dis-tributed stream mining system. The co...
AbstractData-driven, adaptive computations are key to enabling the deployment of accurate and effici...
We consider the problem of resource allocation in mining multiple data streams. Due to the large vol...
Mining data streams has recently become an important and challenging task for a wide range of applic...
Recently, mining from data streams has become an important and challenging task for many real-world ...
Recently, mining from data streams has become an important and challenging task for many real-world ...
Abstract—Emerging stream mining applications require clas-sification of large data streams generated...
In this paper, we propose a distributed solution to the prob-lem of configuring classifier trees in ...
Data streams have become ubiquitous in recent years and are handled on a variety of platforms, rangi...
Data mining (DM) is the process of finding patterns and relationships in databases.The breakthrough ...
Two critical challenges typically associated with mining data streams are concept drift and data con...
Advances in hardware and software in the past decade allow to capture, record and process fast data ...
Modern society produces vast streams of data. Many stream mining algorithms have been developed to c...
Traditional data mining techniques expect all data to be managed within some form of persistent data...