• No se han encontrado resultados

FACIL: Incremental Rule Learning from Data Streams

N/A
N/A
Protected

Academic year: 2021

Share "FACIL: Incremental Rule Learning from Data Streams"

Copied!
13
0
0

Texto completo

(1)

Francisco J. Ferrer Troyano

Jesús S. Aguilar, José C. Riquelme

University of Sevilla

ferrer@lsi.us.es

FACIL:

Incremental Rule Learning

from Data Streams

Contents

ƒ Motivation

ƒ FACIL

ƒ The hybrid knowledge model

ƒ Three user parameters

ƒ IIL updating

ƒ The moderate growth d istance

ƒ Implicit and explicit forgetting heuristics

ƒ The multi-strategy classification method

ƒ Experiments

ƒ Future work

(2)

Motivation

Why Classify? Why Rules?

Heterogeneous Data Sources Æ Noise, Missing Values, Inconsistency High-Speed DataÆ Fast algorithms, approximate answers Open-Ended Data Æ On-line learning… complex models?

FACIL – The hybrid knowledge model

Pres.

0 122

mm / Hg

25 35 75 90

Ritmo

50 210

puls/min

60 80 130 150

e1 a1 e2

a2 a3

e3 Frontera de Decisión

Primer enemigo recibido Amigo obtenido para el 2º enemigo

60 - 150

25-90

Ejemplos Enemigos Ejemplos Amigos

Sop-Pos: 96 % Sop.Rel.=20% Prec.=98.5% Edad 12 - 20

56 Intervalos

Ritmo

50 210

puls/min

e3

a2 a1

Pres.

0 122

mm / Hg

e1 e1

a3

a1 - a3 e1 - e3 21 - 29 30 - 38 39 - 47 48 - 56

e1 a1 e3 a3 a2 e2 12

Border examples inside rules

(3)

FACIL – The Algorithm

Three user parameters

ƒ Maximum number of rules / label

Æ necessary to bound the computational complexity

ƒ Minimum accuracy (purity) / rule: p/(p+n)

Æ anytime,approximate answers

ƒ Maximum growth / dimension

FACIL – IIL updating

IIL: Every example Æ 1/3 cases:

1. Success

2. Anomaly

3. Novelty

General IL Approach

Successive Episodes Æ Updating: Mt= f(Vt,Mt-1) Window of examples Æ Vt: New [and] past examples

(4)

Éxito

0.7 0.6 0.1

0.4 0.5 0.7

A

A

1.0 0.2

a A

e a

FACIL – IIL / Success

0.7 0.6 0.1

0.4 0.5 0.7

1.0 0.2

X

a1 e1

a3

a2

e3

e2

a4

FACIL – IIL / Anomaly

(5)

0.7 0.6 0.1

0.4 0.5 0.7

1.0 0.2

Anomaly

FACIL – Learning Approach / Case 3

Selection:

The rule implying the minimum growth

FACIL – IIL / Novelty: The Growth Distance

0.7 0.9 0.1

0.1 0.5 0.9

1.0

R

X

1.0

d(R,x) = 0.2+0.4

B C

A 0.1 0.4 0.6

D

X

1.0

R R

d(R,x) = 0.25+0.2

(6)

0.7 0.6 0.1

0.4 0.5 0.7

1.0 0.2

0.7 0.6 0.1

0.4 0.5 0.7

R

R

1.0 0.2

Max. Dif. = 15%

G(R

a

,x)= 0.2

G(R

b

,x)= 0.2

X

FACIL – IIL / Novelty: The Moderate Growth Distance

FACIL – Implicit and Explicit Forgetting Heuristics

reglas en t=16

ejemplos t=35

B

A

A B

reglas en t=34

B

A

e16

B

e16, e29

e19, e28

e34

último ejemplo

e28 e19

e16 e16

e34

t=[17,33]

e29

e16 e34

multiple windows

(7)

A A

A B

B

A

FACIL – Multi-strategy Classification

?

A

? ?

A

B

B

B

Experiments – General Purpose Classifier Evaluation

FACIL as a UCI Repository

10 Folds Cross Validation, 10 times, t-student (0.05)

Implicit Forgetting Heuristic

Synthetic Data Streams

Change Magnitude = ±0.1 for 40% attributes every 104examples INPUT: 105examples - 5% class noise: 900 training / 100 testing Both Implicit and Explicit Forgetting Heuristics

(8)

UCI Repository – Accuracy

50 60 70 80 90 100

Balance Breast C, Glass Heart S, Ionosphere Iris Diabetes Sonar Vehicle Vowel Waveform Wine C4.5Rules FACIL

Numerical Attributes

Num. & Symbolic Atts.

40 50 60 70 80 90 100

Anne al

Auto s

Clev elan

d Cred

it Rati ng

Germ an Cr

edit Hep

atitis Horse

Coli

c Zoo

Audo ilogy

Brea st C

ancer Mush

room Prim

ary T umor

Soyb

ean Vote

C4.5Rules FACIL

UCI Repository – Accuracy

(9)

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Balance Breast C, Glass Heart S, Ionosphere Iris Diabetes Sonar Vehicle Vowel Waveform Wine C4.5Rules FACIL

Numerical Attributes

UCI Repository – Number of rules

0 10 20 30 40 50 60 70 80

Anneal Aut

os Clev

eland Cred

it Ra ting

Germ an Cr

edit Hepat

itis Horse

Colic Zoo

Audoi logy

Breas t Ca

ncer Mus

hroom Prim

ary Tum

or Soybean

Vote

C4.5Rules FACIL Num. & Simb. Atts.

UCI Repository – Number of rules

(10)

0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45

10 20 30 40 50 60 70 80 90 100

Valores límite para los tres parámetros

En función del máximo número de reglas En función del máximo crecimiento permitido En función del umbral de mínima pureza

Time (seconds)

UCI Repository – Parametric Sensitivity

81 81,5 82 82,5 83 83,5 84 84,5 85 85,5 86 86,5 87

10 20 30 40 50 60 70 80 90 100

Valores límite para los tres parámetros

En función del máximo número de reglas En función del máximo crecimiento permitido En función del umbral de mínima pureza

Accuracy (%)

UCI Repository – Parametric Sensitivity

(11)

24 29 34 39 44 49 54 59 64 69 74

10 20 30 40 50 60 70 80 90 100

Valores límite para los tres parámetros

En función del máximo número de reglas En función del máximo crecimiento permitido En función del umbral de mínima pureza

Number of rules

UCI Repository – Parametric Sensitivity

Máx. Growth = 50% Máx. Growth = 75%

Nº Attributes Accuracy Time Nº Rules ≤ 25 Accuracy Time Nº Rules ≤ 25

10 96,36 14 10 98.32 20 7

20 93,25 63 9 94.57 35 12

30 91,04 124 3 89.26 53 10

40 89,7 221 2 89.19 67 7

50 83,88 277 4 87.72 77 10

Moving Hyperplane – Accuracy, time, and number of rules

Î>180 examples / second

Î>3500 examples / second Î>2500 examples / second

Î>650 examples / second

(12)

0 50 100 150 200 250 300 350 400 450 500 550 600

10 20 30 40 50

Max Crec. = 50 %, Max Nº Reglas = 50 Max Crec. = 75 %, Max Nº Reglas = 100

Time

Moving Hyperplane – Sensitivity to the number of attributes

73 78 83 88 93 98

1000 10000 20000 30000 40000 50000

Max Crec. = 75 %, Max Nº Reglas = 25 Max Crec. = 100 %, Max Nº Reglas = 10

Accuracy

Moving Hyperplane – Sensitivity to the number of examples

(13)

Sensitivity to attribute

Removing / Recovering attributes To adapt the Growth distance:

Changing attribute weights Æ improve the selection of rules

Reordering attributes according influence Æ speed-up the updating process

Processing streams with a variable number of attributes

To evaluate alternative distances between a rule and an example

PreProcessing streams? Æ Multiple Classification

Future Work

Francisco Ferrer

LSI - University of Seville

ferrer@lsi.us.es

GRAZIE MILLE

Referencias

Documento similar

getNumAttributes() Gets the number of attributes of the dataset considering label attributes and the relational attribute with bags as a single attribute.. getNumAttributesInABag()

GARCIA de JALON (1980) has studied the changes in stream benthic communities caused by the Pinilla reservoir in the Lozoya river (Central Spain), which is used for water

Thus, this paper tackles the problem of wind speed prediction from synoptic pressure patterns by considering wind speed as a discrete variable and, consequently, wind speed

Linked data, enterprise data, data models, big data streams, neural networks, data infrastructures, deep learning, data mining, web of data, signal processing, smart cities,

The first chapter of this thesis, Learning about the international diffusion of durable goods, uses a large data set that includes the diffusion process of 31 durable goods from

The data for learning self-regulation (higher values in this variable mean that the students used more strategies oriented to learning goals) is coming from the

4) Restoration activities led to a significant positive change in the benthic community in terms of spatial and temporal variability. 5) Only the 1-GOLD metric had the capacity

There is no context deformation in this application, but each news item is mapped to a point in the context space, and the relevance of the news is a function of their distance from