Where are we going? Predicting the evolution of individuals

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Siddiqui, ZF (Author) Other; Oliveira, M (Author) Other; João Gama (Author) FEP; Spiliopoulou, M (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 357-368

11th International Symposium on Intelligent Data Analysis, IDA 2012

Helsinki, 25 October 2012 through 27 October 2012

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-008-715

DOI: 10.1007/978-3-642-34156-4_33

Abstract (EN): When searching for patterns on data streams, we come across perennial (dynamic) objects that evolve over time. These objects are encountered repeatedly and each time with different definition and values. Examples are (a) companies registered at stock exchange and reporting their progress at the end of each year, and (b) students whose performance is evaluated at the end of each semester. On such data, domain experts also pose questions on how the individual objects will evolve: would it be beneficial to invest in a given company, given both the company's individual performance thus far and the drift experienced in the model? Or, how will a given student perform next year, given the performance variations observed thus far? While there is much research on how models evolve/change over time [Ntoutsi et al., 2011a], little is done to predict the change of individual objects when the states are not known a priori. In this work, we propose a framework that learns the clusters to which the objects belong at each moment, uses them as ad hoc states in a state-transition graph, and then learns a mixture model of Markov Chains, which predicts the next most likely state/cluster per object. We report on our evaluation on synthetic and real datasets. © Springer-Verlag Berlin Heidelberg 2012.

Language: English

Type (Professor's evaluation): Scientific