Performance evaluation of classification methods in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Handling class imbalance problem in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Annotated probabilistic temporal logic
ACM Transactions on Computational Logic (TOCL)
Forecasting complex group behavior via multiple plan recognition
Frontiers of Computer Science in China
An adaptive approach to chinese semantic advertising
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Terrorist organization behavior prediction algorithm based on context subspace
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Towards group behavioral reason mining
Expert Systems with Applications: An International Journal
Interaction-based group identity detection via reinforcement learning and artificial evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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A proposed framework for predicting a group's behavior associates two vectors with that group. The context vector tracks aspects of the environment in which the group functions; the action vector tracks the group's previous actions. Given a set of past behaviors consisting of a pair of these vectors and given a query context vector, the goal is to predict the associated action vector. To achieve this goal, two families of algorithms employ vector similarity. CONVEXk _NN algorithms use k-nearest neighbors in high-dimensional metric spaces; CONVEXMerge algorithms look at linear combinations of distances of the query vector from context vectors. Compared to past prediction algorithms, these algorithms are extremely fast. Moreover, experiments on real-world data sets show that the algorithms are highly accurate, predicting actions with well over 95-percent accuracy.