TY - JOUR
T1 - Collaborative data stream mining in ibiquitous environment using dynamic classifier selection
AU - DEE Group Author
AU - Sousa, Pedro Alexandre da Costa
PY - 2013/1/1
Y1 - 2013/1/1
N2 - In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.Read More:http://www.worldscientific.com/doi/abs/10.1142/S0219622013500375
AB - In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.Read More:http://www.worldscientific.com/doi/abs/10.1142/S0219622013500375
KW - Collaborative data stream mining
KW - ubiquitous knowledge discovery
KW - performance evaluation
KW - concept drift
U2 - 10.1142/S0219622013500375
DO - 10.1142/S0219622013500375
M3 - Article
VL - 12
SP - 1287
EP - 1308
JO - International Journal Of Information Technology & Decision Making
JF - International Journal Of Information Technology & Decision Making
SN - 0219-6220
IS - 6
ER -