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Fair Use Agreement

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Please get in contact with Prof. Eamonn Keogh, [email protected]

(C) {Ken Ueno, Eamonn Keogh, Xiaopeng Xi}, University of California, Riverside

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Ken UenoKen Ueno Toshiba Corporation, Japan Toshiba Corporation, Japan (( Visiting PostDoc Researcher at UC Riverside Visiting PostDoc Researcher at UC Riverside ))

Xiaopeng XiXiaopeng XiEamonn KeoghEamonn Keogh

Dah-Jye Lee Dah-Jye Lee Brigham Young University, U.S.A. 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.University of California, Riverside, U.S.A.

Draft ver. 12/12/2006

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Outline of the Talk

1. Motivation & BackgroundUsefulness 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 ANNCHow can we convert conventional nearest neighbor classifier

into the anytime version? What’s the critical intuition?

4. Empirical Evaluations

5. Conclusion

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Case Study: Fish Recognition- Application for Video Monitoring System -

Time intervals tend to vary among fish appearances

0 500 1000 1500 2000 2500 300098

98.5

99

99.5

100

Number of instances seen before interruption, S

accu

racy

(%)

Random Test

SimpleRank Test

0 500 1000 1500 2000 2500 300098

98.5

99

99.5

100

Number of instances seen before interruption, S

accu

racy

(%)

Random Test

SimpleRank Test

2.0 sec 27.0 sec

Preliminary experiments with Rotation-Robust DTW [Keogh 05]

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Plausible for Streaming Shape Recognition

Anytime Classifiers

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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 MigrationBiological Shape Recognition

Motion Search

Multimedia Intelligence

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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.

Time

Qua

lity

of

Sol

utio

n Current Solution

Setup Time

STime

Qua

lity

of

Sol

utio

n Current Solution

Setup Time

S1. Suspend

3. ContinueIf you want2. Peek the results

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Anytime Characteristics Interruptability

After some small amount of setup time, the algorithm can be stopped at anytime and provide an answer

MonotonicityThe quality of the result is a non-decreasing function of computation time

Diminishing returnsThe 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]

Time

Qua

lity

of

Sol

utio

n Current Solution

Setup Time

STime

Qua

lity

of

Sol

utio

n Current Solution

Setup Time

S

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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 nectarslike “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?

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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

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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.

.)(,argmax)(ˆ1

k

ii

vq xfvxc

V

:

:

:)(ˆ

:

:

1

k

xc

xxx

x

q

ki

q

V

a query instance

the k instances

estimated class of qx

a set of class labels

# of nearest neighbors

.0

1),(

otherwise

baifba

How can we convert it into anytime algorithm?

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Designing the anytime Nearest Neighboranytime Nearest Neighbor

12345678910111213141516171819202122

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(Indexp) , O); If D < best_match_val best_match_val = D; best_match_class = Database.class_label(Indexp); EndEndDisp(‘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(Indexp) , O); If D < best_match_val best_match_val = D; best_match_class = Database.class_label(Indexp); End p = p +1; user_has_not_interrupted = test_for_user_interrupt;End

Plug-in design for any ordering method

InitialStep

Interruptiblestep

(Constant Time)

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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 SStatic Dynamic

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JF:two-class classification problem

-2 -1 0 1 2

-2

-1

0

1

2

-2 -1 0 1 2

-2

-1

0

1

2

Class AClass A

Class BClass B

2-D Gaussian ball

Hard to classify correctly because of the round shape.

We need non-linear and fast-enough classifier.

.

))(())((

2

1)(

222

2

)(2

2

otherwiseBClass

rymeanyxmeanxifAClass

exfmx

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We cannot use DP for JF problem

I II IIII II III

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.

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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

)acc(DROP3) ,acc(DROP2) OP1),max(acc(DRop)acc(BestDr

NaïveRank Algorithms Sorting based on leave-one-out with 1-Nearest Neighbor

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SimpleRank Ordering

otherwise )1__/(2

)class( )(class if 1)(

j

j

classofnum

xxxrank

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 measure2. sort it in reverse order.

based on NaïveRank Algorithm [Xi and Keogh 06] Sorting by leave-one-out with 1-Nearest Neighbor

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How SimpleRank works.Ranking process on JF Datasetby Simple Rank

Movie ( T = 1 … 50 )

-2 -1 0 1 2

I-2

-1

0

1

2

-2 -1 0 1 2

II-2 -1 0 1 2

I-2

-1

0

1

2

-2 -1 0 1 2

I-2

-1

0

1

2

-2 -1 0 1 2

II-2 -1 0 1 2

II

SimpleRank Random Rank (baseline)

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Click here to start movie

T=10

wrong class estimation area

Voronoi Tessellation on JF Dataset

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Empirical Evaluations

Name #

classes# features # instances Evaluation Data Type

JF 2 2 20,000 2,000/18,000 Real (synthetic)

Australian Credit 2 14 690 10-fold CV Mixed

Letter 26 16 20,000 5,000/15,000 Real

Pen Digits 10 16 10,992 7,494/3,498 Real

Forest Cover Type 7 54 581,012 11,340/569,672 Mixed

Ionosphere 2 34 351 10-fold CV Real

Voting Records 2 16 435 10-fold CV Boolean

Two Patterns 4 128 5,000 1,000/4,000 time series

Leaf 6 150 442 10-fold CV time series

Face 16 131 2,231 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

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0 50 100 150 200 250 300 350

90

100

Number of instances seen before interruption, S

acc

ura

cy(%

)

Random Test

SimpleRank Test

BestDrop Test

K=1: Voting Records

SimpleRank

BestDrop

RandomRank

10-fold Cross Validation, Euclidean

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K=1: Forest Cover Type

0 2000 4000 6000 8000 10000 1200030

35

40

45

50

55

60

65

70

SimpleRank, k=1

Random Rank, k=1

Acc

urac

y (%

)

# of instances seen before interruption

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K=1,3,5 Australian Credit

0 100 200 300 400 500 60040

45

50

55

60

65

70

75

80

85

90

K=1K=3K=5

10-CV, Euclidean

Australian Credit datasetAcc

urac

y (%

)

# of instances seen before interruption

Preliminary Results in our experiments

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K=1 Two Patterns

- Time Series Data -

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Future Research Directions

Make ordering+sorting much faster O(n log n) for sorting + α

Handling Concept Drift Showing Confidence

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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!

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Acknowledgments

Dr. Agenor Mafra-Neto, ISCA Technologies, IncDr. Geoffrey Webb, Monash UniversityDr. Ying Yang, Monash University Dr. Dennis Shiozawa, BYUDr, Xiaoqian Xua, BYUDr. Pengcheng Zhana, BYUDr. Robert Schoenberger, Agris-Schoen Vision Systems, IncJill Brady, UCRNSF grant IIS-0237918

Many Thanks!!

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Fin Thank you for your attention.

Any Question?