Name
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 victimsâ 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 concept 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.5220/0005335700170024 | ICEIS (3-1) |
Field | DocType | Citations |
Online learning,Data mining,Data stream mining,Software deployment,Data stream,Computer science,Concept drift,Prediction algorithms,Artificial intelligence,Adversary,Machine learning,Government | Conference | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anderson José de Souza | 1 | 0 | 0.68 |
| André Pinz Borges | 2 | 14 | 7.67 |
| Heitor Murilo Gomes | 3 | 155 | 17.36 |
| Jean Paul Barddal | 4 | 140 | 16.77 |
| Fabrício Enembreck | 5 | 274 | 38.42 |