TY - JOUR
T1 - CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior
JF - Intelligent Systems, IEEE
Y1 - 2008
A1 - Martinez,V.
A1 - Simari,G. I
A1 - Sliva,A.
A1 - V.S. Subrahmanian
KW - (artificial
KW - algorithm;action
KW - algorithm;behavioural
KW - algorithm;CONVEXk-NN
KW - BEHAVIOR
KW - computing;ontologies
KW - CONVEXMerge
KW - forecasting;high-dimensional
KW - intelligence);
KW - metric
KW - sciences
KW - space;ontology;similarity-based
KW - vector;context
KW - vector;group
AB - 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.
VL - 23
SN - 1541-1672
CP - 4
M3 - 10.1109/MIS.2008.62
ER -