Anytime algorithm development tools
ACM SIGART Bulletin
On state-space abstraction for anytime evaluation of Bayesian networks
ACM SIGART Bulletin
Communications of the ACM
Operating systems (3rd ed.): internals and design principles
Operating systems (3rd ed.): internals and design principles
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Modern Operating Systems
Modern Information Retrieval
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
treeNets: A Framework for Anytime Evaluation of Belief Networks
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Operating Systems (3rd Edition)
Operating Systems (3rd Edition)
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Detecting outliers on arbitrary data streams using anytime approaches
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Techniques for efficient learning without search
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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In many real-world applications of classification learning, such as credit card transaction vetting or classification embedded in sensor nodes, multiple instances simultaneously require classification under computational resource constraints such as limited time or limited battery capacity. In such a situation, available computational resources should be allocated across the instances in order to optimize the overall classification efficacy and efficiency. We propose a novel anytime classification framework, Scheduling Anytime Averaged Probabilistic Estimators (SAAPE), which is capable of classifying a pool of instances, delivering accurate results whenever interrupted and optimizing the collective classification performance. Following the practice of our previous anytime classification system AAPE, SAAPE runs a sequence of very efficient Bayesian probabilistic classifiers to classify each single instance. Furthermore, SAAPE implements seven alternative scheduling schemes to decide which instance gets available computational resources next such that a new classifier can be applied to refine its classification. We formally present each scheduling scheme's definition, rationale and time complexity. We conduct large-scale experiments using 60 benchmark data sets and diversified statistical tests to evaluate SAAPE's performance on zero-one loss classification as well as on probability estimation. We analyze each scheduling scheme's advantages and disadvantages according to both theoretical understandings and empirical observations. Consequently we identify effective scheduling schemes that enable SAAPE to accomplish accurate anytime classification for a pool of instances.