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Authors: Anderson José de Souza ; André Pinz Borges ; Heitor Murilo Gomes ; Jean Paul Barddal and Fabrício Enembreck

Affiliation: Pontifícia Universidade Católica do Paraná, Brazil

ISBN: 978-989-758-096-3

Keyword(s): Data Stream Classification, Crime Forecasting, Public Safety, Concept Drift.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Strategic Decision Support Systems

Abstract: Traditional prediction algorithms assume that the underlying concept is stationary, i.e., no changes are expected to happen during the deployment of an algorithm that would render it obsolete. Although, for many real world scenarios changes in the data distribution, namely concept drifts, are expected to occur due to variations in the hidden context, e.g., new government regulations, climatic changes, or adversary adaptation. In this paper, we analyze the problem of predicting the most susceptible types of victims of crimes occurred in a large city of Brazil. It is expected that criminals change their victims’ types to counter police methods and vice-versa. Therefore, the challenge is to obtain a model capable of adapting rapidly to the current preferred criminal victims, such that police resources can be allocated accordingly. In this type of problem the most appropriate learning models are provided by data stream mining, since the learning algorithms from this domain assume that con cept drifts may occur over time, and are ready to adapt to them. In this paper we apply ensemble-based data stream methods, since they provide good accuracy and the ability to adapt to concept drifts. Results show that the application of these ensemble-based algorithms (Leveraging Bagging, SFNClassifier, ADWIN Bagging and Online Bagging) reach feasible accuracy for this task. (More)

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Paper citation in several formats:
de Souza, A.; Pinz Borges, A.; Gomes, H.; Barddal, J. and Enembreck, F. (2015). Applying Ensemble-based Online Learning Techniques on Crime Forecasting.In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 17-24. DOI: 10.5220/0005335700170024

@conference{iceis15,
author={Anderson José de Souza. and André Pinz Borges. and Heitor Murilo Gomes. and Jean Paul Barddal. and Fabrício Enembreck.},
title={Applying Ensemble-based Online Learning Techniques on Crime Forecasting},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={17-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005335700170024},
isbn={978-989-758-096-3},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Applying Ensemble-based Online Learning Techniques on Crime Forecasting
SN - 978-989-758-096-3
AU - de Souza, A.
AU - Pinz Borges, A.
AU - Gomes, H.
AU - Barddal, J.
AU - Enembreck, F.
PY - 2015
SP - 17
EP - 24
DO - 10.5220/0005335700170024

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