Learning in the presence of concept drift and hidden contexts
Machine Learning
Incremental Induction of Decision Trees
Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
"Constant, constant, multi-tasking craziness": managing multiple working spheres
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Group awareness in distributed software development
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
User action based adaptive learning with weighted bayesian classification for filtering spam mail
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Work-item notifications alert the team collaborating on a work-item about any update to the work-item (e.g., addition of comments, change in status). However, as software professionals get involved with multiple tasks in project(s), they are inundated by too many notifications from the work-item tool. Users are upset that they often miss the notifications that solicit their response in the crowd of mostly useless ones. We investigate the severity of this problem by studying the work-item repositories of two large collaborative projects and conducting a user study with one of the project teams. We find that, on an average, only 1 out of every 5 notifications that are received by the users require a response from them. We propose TWINY - a machine learning based approach to predict whether a notification will prompt any action from its recipient. Such a prediction can help to suitably mark up notifications and to decide whether a notification needs to be sent out immediately or be bundled in a message digest. We conduct empirical studies to evaluate the efficacy of different classification techniques in this setting. We find that incremental learning algorithms are ideally suited, and ensemble methods appear to give the best results in terms of prediction accuracy.