métodos adaptativos de minería de datos y aprendizaje para ...abifet/santander.pdf · minería de...

Métodos Adaptativos de Minería de Datos y Aprendizajepara Flujos de Datos.

Albert Bifet

LARCA: Laboratori d’Algorismica Relacional, Complexitat i AprenentatgeDepartament de Llenguatges i Sistemes Informàtics

Universitat Politècnica de Catalunya

Junio 2009, Santander

Minería de Datos y Aprendizaje para Flujosde Datos con Cambio de Concepto

La Desintegración de laPersistencia de la Memoria

1952-54

Salvador Dalí

Extraer información de

secuencia potencialmenteinfinita de data

datos que varian con eltiempo

usando pocos recursos

usando ADWIN

ADaptive Sliding WINdow:Ventana deslizanteadaptativa

sin parámetros

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Minería de Datos Masivos

Explosión de Datos en los últimos años

: 100 millones búsquedas por día

: 20 millones transacciones por día

1,000 millones de transacciones de tarjetas de credito por mes

3,000 millones de llamadas telefónicas diarias en EUA

30,000 millones de e-mails diarios, 1,000 millones de SMS

Tráfico de redes IP: 1,000 millones de paquetes por hora porrouter

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Datos Masivos2007

Universo Digital: 281 exabytes (mil millones de gigabytes)

La cantidad de información creada excedió el almacenajedisponible por primera vez

Green Computing

Estudio y práctica de como usar recursos informáticoseficientemente.

Algorithmic Efficiency

Una de las principales maneras de hacer Green Computing

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Koichi KawanaSimplicidad significa conseguir el máximo efecto con losmínimos medios.

Donald Knuth“... we should make use of the idea oflimited resources in our own education.We can all benefit by doing occasional"toy" programs, when artificialrestrictions are set up, so that we areforced to push our abilities to the limit. “

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Introducción: Data Streams

Data Streams

Secuencia potencialmente infinita

Gran cantidad de datos: espacio sublineal

Gran velocidad de llegada: tiempo sublineal por ejemplo

Cada vez que un elemento de un data stream se ha procesado,se descarta o se archiva

Puzzle: Encontrar números que faltan

Sea π una permutación of {1, . . . ,n}.

Sea π−1 la permutación π con unelemento que falta.

π−1[i] llega en orden creciente

Tarea: Determinar el número que falta

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Data Streams










Usar un vectorn-bit paramemorizar todoslos numeros(espacio O(n) )

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Data Streams










Data Streams:espacioO(log(n)).

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Data Streams










Almacenar

n(n +1)

2−∑

j≤iπ−1[j].

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Introducción: Data StreamsProblema

12,35,21,42,5,43,57,2,45,67

Dados n números no ordenados, encontrar un número queesté en la mitad superior de la lista ordenada.

2,5,12,21,35 42,43,45,57,67

AlgoritmoElegir k números aleatoriamente. Devolver el número mayor.

Análisis

La probabilidad de que la solución sea incorrecta es laprobabilidad de que todos los k números estén en la mitadinferior : (1/2)k

Para tener probabilidad δ usaremos k = log1/δ muestras

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Outline

1 Introduction

2 ADWIN : Concept Drift Mining

3 Hoeffding Adaptive Tree

4 Conclusions

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Data Streams

Data StreamsAt any time t in the data stream, we would like the per-itemprocessing time and storage to be simultaneouslyO(logk (N, t)).

Approximation algorithms

Small error rate with high probability

An algorithm (ε,δ )−approximates F if it outputs F̃ for whichPr[|F̃ −F |> εF ] < δ .

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Data Streams Approximation Algorithms

Frequency momentsFrequency moments of a stream A = {a1, . . . ,aN}:

Fk =v

∑i=1

f ki

where fi is the frequency of i in the sequence, and k ≥ 0

F0: number of distinct elements on the sequence

F1: length of the sequence

F2: self-join size, the repeat rate, or as Gini’s index ofhomogeneity

Sketches can approximate F0,F1,F2 in O(logv + logN) space.

Noga Alon, Yossi Matias, and Mario Szegedy.The space complexity of approximationthe frequency moments. 1996

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1011000111 1010101

Sliding WindowWe can maintain simple statistics over sliding windows, usingO(1

εlog2 N) space, where

N is the length of the sliding window

ε is the accuracy parameter

M. Datar, A. Gionis, P. Indyk, and R. Motwani.Maintaining stream statistics over sliding windows. 2002

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10110001111 0101011






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101100011110 1010111






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1011000111101 0101110






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10110001111010 1011101






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101100011110101 0111010






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Outline

1 Introduction



4 Conclusions

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Data Mining Algorithms with Concept Drift

No Concept Drift

-input output

DM Algorithm

-

Counter1

Counter2

Counter3

Counter4

Counter5

Concept Drift

-input output

DM Algorithm

Static Model

-

Change Detect.-

6

�

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Data Mining Algorithms with Concept Drift

No Concept Drift

-input output

DM Algorithm

-

Counter1

Counter2

Counter3

Counter4

Counter5

Concept Drift

-input output

DM Algorithm

-

Estimator1

Estimator2

Estimator3

Estimator4

Estimator5

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Time Change Detectors and Predictors: AGeneral Framework

-xt

Estimator

-Estimation

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-xt

Estimator

-Estimation

- -Alarm

Change Detect.

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-xt

Estimator

-Estimation

- -Alarm

Change Detect.

Memory-

6

6?

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Window Management Models

W = 101010110111111

Equal & fixed sizesubwindows

1010 1011011 1111

[Kifer+ 04]

Equal size adjacentsubwindows

1010101 1011 1111

[Dasu+ 06]

Total window againstsubwindow

10101011011 1111

[Gama+ 04]

ADWIN: All Adjacent subwindows

1 01010110111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


10 1010110111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


101 010110111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


1010 10110111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


10101 0110111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


101010 110111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


1010101 10111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


10101011 0111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


101010110 111111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


1010101101 11111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


10101011011 1111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


101010110111 111

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


1010101101111 11

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W = 101010110111111


1010 1011011 1111

[Kifer+ 04]


1010101 1011 1111

[Dasu+ 06]


10101011011 1111

[Gama+ 04]


10101011011111 1

11

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Algorithm ADWIN

Example

W= 101010110111111W0= 1

ADWIN: ADAPTIVE WINDOWING ALGORITHM

1 Initialize Window W2 for each t > 03 do W ←W ∪{xt} (i.e., add xt to the head of W )4 repeat Drop elements from the tail of W5 until |µ̂W0− µ̂W1 | ≥ εc holds6 for every split of W into W = W0 ·W17 Output µ̂W

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Algorithm ADWIN

Example

W= 101010110111111W0= 1 W1 = 01010110111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 10 W1 = 1010110111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 101 W1 = 010110111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 1010 W1 = 10110111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 10101 W1 = 0110111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 101010 W1 = 110111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 1010101 W1 = 10111111



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Algorithm ADWIN

Example

W= 101010110111111W0= 10101011 W1 = 0111111



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Algorithm ADWIN

Example

W= 101010110111111 |µ̂W0− µ̂W1 | ≥ εc : CHANGE DET.!

W0= 101010110 W1 = 111111



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Algorithm ADWIN

Example

W= 101010110111111 Drop elements from the tail of WW0= 101010110 W1 = 111111



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Algorithm ADWIN

Example

W= 01010110111111 Drop elements from the tail of WW0= 101010110 W1 = 111111



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Algorithm ADWIN [BG07]

ADWIN has rigorous guarantees (theorems)

On ratio of false positives

On ratio of false negatives

On the relation of the size of the current window and changerates

Other methods in the literature: [Gama+ 04], [Widmer+ 96],[Last 02] don’t provide rigorous guarantees.

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Algorithm ADWIN [BG07]

TheoremAt every time step we have:

1 (Few false positives guarantee) If µt remains constant within W,the probability that ADWIN shrinks the window at this step is atmost δ .

2 (Few false negatives guarantee) If for any partition W in twoparts W0W1 (where W1 contains the most recent items) we have|µW0 −µW1 |> ε, and if

ε ≥ 4 ·

√3max{µW0 ,µW1}

min{n0,n1}ln

4nδ

then with probability 1−δ ADWIN shrinks W to W1, or shorter.

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Outline

1 Introduction



4 Conclusions

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Classification

Data set thatdescribes e-mailfeatures fordeciding if it isspam.

Example

Contains Domain Has Time“Money” type attach. received spam

yes com yes night yesyes edu no night yesno com yes night yesno edu no day nono com no day noyes cat no day yes

Assume we have to classify the following new instance:Contains Domain Has Time“Money” type attach. received spam

yes edu yes day ?

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Classification

Assume we have to classify the following new instance:Contains Domain Has Time“Money” type attach. received spam

yes edu yes day ?

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Decision Trees

Basic induction strategy:

A← the “best” decision attribute for next node

Assign A as decision attribute for node

For each value of A, create new descendant of node

Sort training examples to leaf nodes

If training examples perfectly classified, Then STOP, Else iterateover new leaf nodes

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Hoeffding Tree / CVFDT

Hoeffding Tree : VFDT

Pedro Domingos and Geoff Hulten.Mining high-speed data streams. 2000

With high probability, constructs an identical model that atraditional (greedy) method would learn

With theoretical guarantees on the error rate

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VFDT / CVFDT

Concept-adapting Very Fast Decision Trees: CVFDT

G. Hulten, L. Spencer, and P. Domingos.Mining time-changing data streams. 2001

It keeps its model consistent with a sliding window of examples

Construct “alternative branches” as preparation for changes

If the alternative branch becomes more accurate, switch of treebranches occurs

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Decision Trees: CVFDT

No theoretical guarantees on the error rate of CVFDT

CVFDT parameters :

1 W : is the example window size.

2 T0: number of examples used to check at each node if thesplitting attribute is still the best.

3 T1: number of examples used to build the alternate tree.

4 T2: number of examples used to test the accuracy of thealternate tree.

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Decision Trees: Hoeffding Adaptive Tree

Hoeffding Adaptive Tree:

replace frequency statistics counters by estimators

don’t need a window to store examples, due to the fact that wemaintain the statistics data needed with estimators

change the way of checking the substitution of alternatesubtrees, using a change detector with theoretical guarantees

Summary:

1 Theoretical guarantees

2 No Parameters

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What is MOA?

{M}assive {O}nline {A}nalysis is a framework for online learningfrom data streams.

It is closely related to WEKA

It includes a collection of offline and online as well as tools forevaluation:

boosting and baggingHoeffding Trees

with and without Naïve Bayes classifiers at the leaves.

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Ensemble Methods

http://www.cs.waikato.ac.nz/∼abifet/MOA/

New ensemble methods:

ADWIN bagging: When a change is detected, the worst classifieris removed and a new classifier is added.

Adaptive-Size Hoeffding Tree bagging

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Outline

1 Introduction



4 Conclusions

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Conclusions

Adaptive and parameter-free methods based in

replace frequency statistics counters by ADWIN

don’t need a window to store examples, due to the fact that wemaintain the statistics data needed with ADWINs

using ADWIN as change detector with theoretical guarantees,

Summary:

1 Theoretical guarantees

2 No parameters needed

3 Higher accuracy

4 Less space needed

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