1. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Crime Forecasting Using Boosted Ensemble Classifiers Department of Computer Science University of Massachusetts Boston 2012 GRADUATE STUDENTS SYMPOSIUM Present by: Chung-Hsien Yu Advisor: Prof. Wei Ding

2. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Retaining spatiotemporal knowledge by applying multi- clustering to monthly aggregated crime data. Training baseline learners on these clusters obtained from clustering. Adapting a greedy algorithm to find a rule-based ensemble classifier during each boosting round. Pruning the ensemble classifier to prevent it from overfitting. Constructing a strong hypothesis based on these ensemble classifiers obtained from each round. Abstract 2

3. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Original Data 3 Residential Burglary 911 Calls Arrest Foreclosure Street Robbery

4. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Aggregated Data 4 3 1 1 1

5. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Monthly Data 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 2 6 1 0 5 6 6 2 7 5 3 3 1 3 4 4 3 1 4 0 4 3 3 2 8 9 4 0 6 4 5 1 2 3 2 3 0 3 0 2 0 1 2 5 0 0 0 0 5

6. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Monthly Clusters (k=3) 6

7. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Monthly Clusters (k=4) 7

8. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Flow Chart 8

9. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Algorithm (Part I) 9

10. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Algorithm (Part II) 10

11. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Confidence Value 11

12. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Objective Function 12

13. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Minimum Z Value 13

14. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu BuildChain Function 14

15. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu PruneChain Function 15 Loss Function:

16. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Update Weights 16

17. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Strong Hypothesis 17

18. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu 1.The grid cells with the similar crime counts clustered together also are close to each other on the map geographically. Besides, the high-crime-rate area and low- crime-rate area are separated with cluster. 2.The original data set is randomly divided into two subsets each round. The greedy weak-learn algorithm adapts confidence-rate evaluation to “chain” the base-line classifiers using one data set. And then, “trim” the chain using the other data set. 3.The strong hypothesis is easy to calculate. SUMMARY 18

19. Crime Forecasting Using Boosted Ensemble Classifiers Chung-Hsien Yu Q & A THANK YOU!! 19