WEKAA Practical Machine Learning Tool

elbowcheepAI and Robotics

Oct 15, 2013 (3 years and 8 months ago)

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WEKA: A Practical Machine Learning Tool

WEKA

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WEKA: A Practical Machine Learning Tool

Contents


1.Introduction to Weka


2.Explorer


3.Other three main tools


4.Conclusions


5.Reference


WEKA: A Practical Machine Learning Tool

Introduction


What is Weka?



In nature
: A flightless bird with an inquisitive nature found only
on the islands of New Zealand.


Actually
: A practical machine learning tool developed by the
University of Waikato in New Zealand. It is short for
W
aikato
E
nvironment for
K
nowledge
A
nalysis.


Definition
: A collection of machine learning algorithms for data
mining tasks.


Language
: It is written in Java and runs on almost any platform.








Usage
: The algorithms can either be applied:




(1) directly to a dataset (without writing any codes);




(2) called from your own Java code.

WEKA: A Practical Machine Learning Tool

Introduction


Weka consists of


Explorer


Experimenter


Knowledge flow


Simple
C
ommand
L
ine
I
nterface(CLI)


Other tools and Visualization


Java interface


WEKA: A Practical Machine Learning Tool

Explorer


WEKA’s main graphical user interface


Gives access to all its facilities using menu selection and form
filling.(Data
-
Preprocess/Classify/Cluster/Associate/Select
Attributes/Visualize)



1.Data


2. Operations of Explorer with a Classification example.


WEKA: A Practical Machine Learning Tool

Explorer


Data(1)


From files: CSV, ARFF, C4.5…

no
*
.xls



Data loaded from URL or DB


*.xls

*.csv

Attribute
-
Class Attribute

Instance

Instances

Tips

weather.arff ( C:/Program Files/Weka/data/ )

WEKA: A Practical Machine Learning Tool

Explorer


Data(2)

ARFF(
A
ttribute
-
R
elation
F
ile
F
ormat)


@relation <relation
-
name>


@attribute <attribute
-
name> <datatype>


numeric (real or integer numbers)


<nominal
-
specification>


string


date [<date
-
format>]


@data


% notes

More details:

http://www.cs.waikato.ac.nz/

~ml/weka/arff.html

WEKA: A Practical Machine Learning Tool

Explorer


Operations with an example

Input data

Data preprocess

Choose classifier


Test
options

Run

Result analysis

WEKA: A Practical Machine Learning Tool

Explorer

Input data

Summary Statistics

Select an attribute

Visualization

WEKA: A Practical Machine Learning Tool

Explorer

Tune Parameters

Select a Filter

Weka Filter

Apply the Filter

WEKA: A Practical Machine Learning Tool

Explorer

Tune Parameters

Select a Classifier

Decide how to evaluate

Model list

Results

WEKA: A Practical Machine Learning Tool

Right
-
click on model to get

Menu (save, visualize, etc)

WEKA: A Practical Machine Learning Tool

WEKA: A Practical Machine Learning Tool

Others


Experimenter


Comparing different learning algorithms


------
on different datasets


------
with various parameter settings


------
and analyzing the performance statistics





Click it for Experimenter

WEKA: A Practical Machine Learning Tool

Others


KnowledgeFlow


The KnowledgeFlow provides an alternative to the Explorer as a
graphical front end to Weka's core algorithms.


The KnowledgeFlow is a work in progress so some of the
functionality from the Explorer is not yet available.



Click it for KnowledgyFlow

WEKA: A Practical Machine Learning Tool

Others


Simple command line interface


All implementations of the algorithms have a uniform command
-
line interface.


java weka.classifiers.trees.J48
-
t weather.arff



Click it for Simple CLI

WEKA: A Practical Machine Learning Tool

Conclusions

1.Explorer:


Input data

Data preprocess

Choose classifier


Test options

Run

Result analysis

2.Experimenter:


It is necessary for further studies.

3.Make full use of



1. Java tips;


2. WekaManual.pdf; (C:/Program Files/Weka/ )


3. Play it yourself!


WEKA: A Practical Machine Learning Tool

Reference


Mitchell, T. Machine Learning, 1997 McGraw Hill.


Ian H. Witten, Eibe Frank, Len Trigg, Mark Hall, Geoffrey Holmes, and Sally Jo
Cunningham (1999). Weka: Practical machine learning tools and techniques with Java
implementations.


Ian H. Witten, Eibe Frank (2005). Data Mining: Practical Machine Learning Tools
and Techniques (Second Edition, 2005). San Francisco: Morgan Kaufmann


Weka Homepage:
http://www.cs.waikato.ac.nz/~ml/weka/


Wekawiki:
http://weka.wikispaces.com/


Weka on SourceForge.net:
http://sourceforge.net/projects/weka


WekaManual.pdf (C:
\
Program Files
\
Weka
-
3
-
6
\
WekaManual.pdf)