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

3. Outline of the Talk • Motivation & BackgroundUsefulness of the anytime nearest neighbor classifierfor real world applications including fish shape recognition. • Anytime Nearest Neighbor Classifier (ANNC) • SimpleRank, the critical ordering method for ANNCHow can we convertconventional nearest neighbor classifierinto the anytime version? What’s the critical intuition? • Empirical Evaluations • Conclusion

4. - - 2 2 - - 1.5 1.5 - - 1 1 - - 0.5 0.5 0 0 0.5 0.5 1 1 1.5 1.5 2 2 2.0 sec 27.0 sec Case Study: Fish Recognition- Application for Video Monitoring System - Preliminary experiments with Rotation-Robust DTW [Keogh 05] Time intervals tend to vary among fish appearances Anytime Classifiers Plausible for Streaming Shape Recognition

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? Motion Search Fish Migration Biological Shape Recognition Multimedia Intelligence Medical Diagnosis

6. 3. Continue If you want 2. Peek the results 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

7. Anytime Characteristics • InterruptabilityAfter 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 • PreemptabilityThe algorithm can be suspended and resumed with minimal overhead [Zilberstein and Russell 95]

8. Bumble Bee’s Anytime Strategy 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.” Lars Chittka, Adrian G. Dyer, Fiola Bock, Anna Dornhaus, Nature Vol.424, 24 Jul 2003, p.388 Big Question: How can we make classifiers wiser / more rapid like bees?

9. Nearest Neighbor Classifiers Anytime Algorithm + Lazy Learning [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

10. a query instance the k instances estimated class of a set of class labels # of nearest neighbors 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. How can we convert it into anytime algorithm?

11. How can we make good Index for training data? Designing the anytime Nearest Neighbor Initial Step (Constant Time) Interruptible step Plug-in design for any ordering method

12. Numerosity Reduction: S must be decidablebefore classification Anytime Preprocessing: S does not need to be decidablebefore classification Static  Dynamic Keypoint: in terms of interrupting time S 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

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

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

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 AlgorithmsSorting based on leave-one-out with 1-Nearest Neighbor

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

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

18. Empirical Evaluations fair evaluations based on diverse kinds of datasets 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

19. 100 accuracy(%) 90 Random Test SimpleRank Test BestDrop Test 0 50 100 150 200 250 300 350 Number of instances seen before interruption, S K=1: Voting Records 10-fold Cross Validation, Euclidean SimpleRank BestDrop RandomRank

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

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

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

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

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 highestaccuracy in diverse datasets. • Demonstrates the usefulness for shape recognitionin Stream Video Mining. Good Job! This is the best-so-far ordering method familiar with anytime Nearest Neighbor! kNN SimpleRank

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. Fin Thank you for your attention. Any Question?