CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior

MARIA VANINA MARTINEZ; GERARDO I. SIMARI; AMY SLIVA; V.S. SUBRAHMANIAN

IEEE INTELLIGENT SYSTEMS

IEEE COMPUTER SOC

Lugar: Los Alamitos, CA, USA; Año: 2008 vol. 23 p. 51 - 57

1541-1672

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.