1. 1 Fair Use Agreement This agreement covers the use of this presentation, please read carefully. You may freely use these slides for teaching, if You send me an email telling me the class number/ university in advance. My name and email address appears on the first slide (if you are using all or most of the slides), or on each slide (if you are just taking a few slides). You may freely use these slides for a conference presentation, if You send me an email telling me the conference name in advance. My name appears on each slide you use. You may not use these slides for tutorials, or in a published work (tech report/ conference paper/ thesis/ journal etc). If you wish to do this, email me first, it is highly likely I will grant you permission. Please get in contact with Prof. Eamonn Keogh, eamonn@cs.ucr.edu (C) {Ken Ueno, Eamonn Keogh, Xiaopeng Xi}, University of California, Riverside

2. 2 Ken Ueno Toshiba Corporation, Japan ( Visiting PostDoc Researcher at UC Riverside ) ( Visiting PostDoc Researcher at UC Riverside ) Xiaopeng Xi Eamonn Keogh Dah-Jye Lee Brigham Young University, U.S.A. Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining University of California, Riverside, U.S.A. Draft ver. 12/12/2006

3. 3 Outline of the Talk 1. Motivation & Background Usefulness of the anytime nearest neighbor classifier for real world applications including fish shape recognition. 2. Anytime Nearest Neighbor Classifier (ANNC) 3. SimpleRank, the critical ordering method for ANNC How can we convert conventional nearest neighbor classifier into the anytime version? What’s the critical intuition? 4. Empirical Evaluations 5. Conclusion

4. 4 Case Study : Fish Recognition - Application for Video Monitoring System - Time intervals tend to vary among fish appearances 2.0 sec 27.0 sec Preliminary experiments with Rotation-Robust DTW [Keogh 05] -2-1.5-1-0.50 11.52-2- -1-0.50 11.52 Plausible for Streaming Shape Recognition Anytime Classifiers

5. 5 Real World Problems for Data Mining When will it be finished? Challenges for Data Mining in Real World Applications. Accuracy / Speed  Trade Off Limited memory space Real time processing Best-so-far Answer Available anytime? Medical Diagnosis Fish Migration Biological Shape Recognition Motion Search Multimedia Intelligence

6. 6 Anytime Algorithms Trading execution time for quality of results. Always has a best-so-far answer available. Quality of the answer improves with execution time. Allowing users to suspend the process during execution, and keep going if needed. 1. Suspend 3. Continue If you want 2. Peek the results

7. 7 Anytime Characteristics Interruptability After some small amount of setup time, the algorithm can be stopped at anytime and provide an answer Monotonicity The quality of the result is a non-decreasing function of computation time Diminishing returns The improvement in solution quality is largest at the early stages of computation, and diminishes over time Measurable Quality The quality of an approximate result can be determined Preemptability The algorithm can be suspended and resumed with minimal overhead [Zilberstein and Russell 95]

8. 8 Bumble Bee’s Anytime Strategy Lars Chittka, Adrian G. Dyer, Fiola Bock, Anna Dornhaus, Nature Vol.424, 24 Jul 2003, p.388 To survive I can perform the best judgment for finding real nectars like “anytime learning” ! “Bumblebees can choose wisely or rapidly, but not both at once.” Big Question: How can we make classifiers wiser / more rapid like bees?

9. 9 Nearest Neighbor Classifiers [Reasons] To the best of our knowledge there is no “Anytime Nearest Neighbor Classifier” so far. Inherently familiar with similarity measures. Easily handle time series data by using DTW. Robust & accurate Anytime Algorithm + Lazy Learning

10. 10 Nearest Neighbor Classifiers Instance-based, lazy classification algorithm based on training exemplars. Giving the class label of the closest training exemplars with unknown instance based on a certain distance measure. As for k-Nearest Neighbor (k-NN) we give the answer by voting. a query instance the k instances estimated class of a set of class labels # of nearest neighbors How can we convert it into anytime algorithm?

11. 11 anytime Nearest Neighbor Designing the anytime Nearest Neighbor 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Function [best_match_class]= Anytime_Classifier (Database, Index, O) best_match_val = inf; best_match_class = undefined; For p = 1 to number_of_classes(Database) D = distance(Database.object(Index p ), O); If D < best_match_val best_match_val = D; best_match_class = Database.class_label(Index p ); End Disp(‘The algorithm can now be interrupted’); p = number_of_classes(Database) + 1; While (user_has_not_interrupted AND p < max(index) ) D = distance(Database.object(Index p ), O); If D < best_match_val best_match_val = D; best_match_class = Database.class_label(Index p ); End p = p +1; user_has_not_interrupted = test_for_user_interrupt; End Plug-in design for any ordering method Initial Step Interruptible step How can we make good Index for training data? (Constant Time)

12. 12 Tentative Solution for good ordering Ordering Training Data is critical. Critical points for classification results best first or worst last?  put non-critical points last. Numerosity Reduction can partially be the good ordering solutions. The problem is very similar to ordering problem for anytime algorithms. Leave-one-out (k=1) within training data Numerosity Reduction: S must be decidable before classification Anytime Preprocessing: S does not need to be decidable before classification Keypoint: in terms of interrupting time S Static  Dynamic

13. 13 JF:two-class classification problem Class A Class B 2-D Gaussian ball Hard to classify correctly because of the round shape. We need non-linear and fast-enough classifier.

14. 14 We cannot use DP for JF problem Dynamic Programming (DP) ans(n-1)  ans(n) Ideal Tessellations heavily depend on entire feature space. Captures the entire classification boundaries in the early stage. DP is locally optimal.

15. 15 Numerosity reduction Scoring strategy: similar to Numerosity Reduction Random Ranking (baseline) DROP Algorithms [Wilson and Martinez 00] Weighting based on enemies / associates for Nearest Neighbor DROP1, DROP2,DROP3 NaïveRank Algorithms Sorting based on leave-one-out with 1-Nearest Neighbor

16. 16 SimpleRank Ordering Penalizing the close instance with the different class label. Adjust the penalty weights with regard to the num. of Classes Observation 1 Observation 2 NaiveRank Anytime Framework + SimpleRank 1. order training instances by the unimportance measure 2. sort it in reverse order. based on NaïveRank Algorithm [Xi and Keogh 06] Sorting by leave-one-out with 1-Nearest Neighbor

17. 17 How SimpleRank works. Ranking process on JF Dataset by Simple Rank Movie ( T = 1 … 50 ) SimpleRank Random Rank (baseline) Click here to start movie T=10 wrong class estimation area Voronoi Tessellation on JF Dataset

18. 18 Empirical Evaluations Name# classes# features# instancesEvaluationData Type JF2220,000 2,000/18,000 Real (synthetic) Australian Credit214690 10-fold CV Mixed Letter261620,000 5,000/15,000 Real Pen Digits101610,992 7,494/3,498 Real Forest Cover Type754581,012 11,340/569,672 Mixed Ionosphere234351 10-fold CV Real Voting Records216435 10-fold CV Boolean Two Patterns41285,000 1,000/4,000 time series Leaf6150442 10-fold CV time series Face161312,231 1,113/1,118 time series All of the datasets are public and available for everyone! UCI ICS Machine Learning Data Archive UCI KDD Data Archive UCR Time Series Data Mining Archive fair evaluations based on diverse kinds of datasets

19. 19 050100150200250300350 90 100 Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test BestDrop Test K=1: Voting Records SimpleRank BestDrop RandomRank 10-fold Cross Validation, Euclidean

20. 20 K=1: Forest Cover Type Accuracy (%) # of instances seen before interruption

21. 21 K=1,3,5 Australian Credit 10-CV, Euclidean Australian Credit dataset Accuracy (%) # of instances seen before interruption Preliminary Results in our experiments

22. 22 K=1 Two Patterns - Time Series Data -

23. 23 Future Research Directions Make ordering+sorting much faster O(n log n) for sorting + α Handling Concept Drift Showing Confidence

24. 24 Conclusion and Summary Our Contributions: - New framework for Anytime Nearest Neighbor. - SimpleRank: Quite simple but critically good ordering. So far our method has achieved the highest accuracy in diverse datasets. Demonstrates the usefulness for shape recognition in Stream Video Mining. Good Job! This is the best-so-far ordering method familiar with anytime Nearest Neighbor!

25. 25 Acknowledgments Dr. Agenor Mafra-Neto, ISCA Technologies, Inc Dr. Geoffrey Webb, Monash University Dr. Ying Yang, Monash University Dr. Dennis Shiozawa, BYU Dr, Xiaoqian Xua, BYU Dr. Pengcheng Zhana, BYU Dr. Robert Schoenberger, Agris-Schoen Vision Systems, Inc Jill Brady, UCR NSF grant IIS-0237918 Many Thanks!!

26. 26 Fin Thank you for your attention. Any Question?