Authors:
Anderson José de Souza
;
André Pinz Borges
;
Heitor Murilo Gomes
;
Jean Paul Barddal
and
Fabrício Enembreck
Affiliation:
Pontifícia Universidade Católica do Paraná, Brazil
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 co
ncept 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.
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