1. Machine Learning with WEKA

2. Copyright: Martin Kramer (mkramer@wxs.nl)
WEKA: the bird
Copyright: Martin Kramer
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3. WEKA: the software
Machine learning/data mining software written in Java (distributed under the GNU Public License)
Used for research, education, and applications
Complements “Data Mining” by Witten & Frank
Main features:
Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods
Graphical user interfaces (incl. data visualization)
Environment for comparing learning algorithms
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4. WEKA: versions There are several versions of WEKA:
WEKA 3.0: “book version” compatible with description in data mining book
WEKA 3.2: “GUI version” adds graphical user interfaces (book version is command-line only)
WEKA 3.3: “development version” with lots of improvements
This talk is based on WEKA 3.4
WEKA 3.x
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5. WEKA only deals with “flat” files
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
63,male,typ_angina,233,no,not_present
67,male,asympt,286,yes,present
67,male,asympt,229,yes,present
38,female,non_anginal,?,no,not_present
...
Flat file in
ARFF format
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6. WEKA only deals with “flat” files
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
63,male,typ_angina,233,no,not_present
67,male,asympt,286,yes,present
67,male,asympt,229,yes,present
38,female,non_anginal,?,no,not_present
...
numeric attribute
nominal attribute
class attribute
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8. Explorer: pre-processing the data
Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary
Data can also be read from a URL or from an SQL database (using JDBC)
Pre-processing tools in WEKA are called “filters”
WEKA contains filters for:
Discretization, normalization, attribute selection , resampling, transforming and combining attributes, …
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9. 3 1 2 5 4 Find your data folder open iris.arff data set
make sure file extension is .arff
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10. The iris Dataset (鳶尾花資料集)
Sources (Perhaps the best known dataset in pattern recognition)
Creator: R.A. Fisher
Donor: Michael Marshall
Date: July, 1988
Number of Instances: 150 (50 in each of 3 classes)
Number of Input Attributes: 4, numeric
Missing Attribute Values: None
Attribute Information
sepal length (in cm) 花萼長度
sepal width (in cm)
petal length (in cm) 花瓣長度
petal width (in cm)
class: iris Setosa, iris Versicolour, iris Virginica (3 classes)
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11. 鳶尾花經典分類
Iris Versicolour
Iris Setosa
Iris Virginica
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12. 鳶尾花(iris, 愛麗絲) 科別：鳶尾科多年生草本 生長適溫約攝氏20~28度 特性： iris源於希臘語，意思是彩虹
有根莖鳶尾和球莖鳶尾之分
高30~40公分
葉劍形帶狀
三片花瓣，多為藍或紫色
三片花蕚，多為藍或紫色
《鳶尾花》是梵谷的知名作品之一
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13. 鳶尾花
三片花瓣為白色，三片花萼為藍紫褐色
花莖外形與葉片相似
叢生，葉為細劍形
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15. Save as other file format as needed
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18. Choose petallength 2018/9/15 University of Waikato

19. click
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20. click
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21. Click to change parameters
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23. Click to read more description
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27. Explorer: building “classifiers”
Classifiers in WEKA are models to predict nominal or numeric quantities for a dependent variable
Implemented learning schemes include:
Decision trees, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, …
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30. Click to change parameters
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40. Scroll to see other results
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42. Right click
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44. Right click
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46. Click to change Y coordinate Click to change Y coordinate
Classification errors
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51. 2 1
output neurons
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52. 1 2 hidden neurons & connection weights 2018/9/15
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53. 2 1 Click to change parameters input neurons & connection weights
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54. 1 2 3
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62. ROC curve
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63. Use a numeric attribute as output
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64. Choose petallength
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66. 1 2 Use M5P to predict petal length of a iris flower
M5 Model trees and rules:
Combines a decision tree with linear regression
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67. 3 1 2 When to use model 1, 2, 3 Model 1 Model 2 Model 3
Generate 3 models
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68. Linear Model 1 Linear Model 2 Linear Model 3 2018/9/15
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69. right click “Visualize classifier error”
performance
evaluation
right click “Visualize classifier error”
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70. Click a data point to show the data window
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72. 2 1
choose
right click
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74. Explorer: clustering data
WEKA contains “clusterers” for finding groups of similar instances in a dataset
Implemented schemes are:
k-Means, EM, Cobweb, X-means, FarthestFirst
Clusters can be visualized and compared to “true” clusters (if given)
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75. 1 2
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77. 3 1 2
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78. Enter 3 clusters
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80. Clusters can be compared to “true” clusters (if given)
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81. Scroll down
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83. Right click
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85. Incorrectly classified instances
Click to change Y coordinate
Click to change Y coordinate
Incorrectly classified instances
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86. Explorer: finding associations
WEKA contains an implementation of the Apriori algorithm for learning association rules
Works only for discrete (categorical) data
Can identify statistical dependencies between groups of attributes:
milk, butter  bread, eggs (with confidence 0.9 and support 2000)
Apriori can compute all rules that have a given minimum support and exceed a given confidence
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89. Load vote data set
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90. The Congressional Voting Records Dataset (美國國會選舉資料集)
Sources (Date: April, 1987)
Origin : Congressional Quarterly Almanac, 98th Congress, 2nd session 1984
Donor: Jeff Schlimmer
Area : Social
Data Set Characteristics : Multivariate
Associated Task : Classification
Number of Instances: 435
Number of Input Attributes: 16, Categorical
Number of classes : 2 (Republican or Democrat)
Missing Attribute Values: Yes
An unknown value is encoded as ?
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91. The soybean-large Dataset (黃豆資料集)
Sources (Date: July, 1988)
Origin : R.S. Michalski and R.L. Chilausky
Donor: Ming Tan & Jeff Schlimmer
Purpose : for soybean disease diagnosis
Data Set Characteristics : Multivariate
Associated Task : Classification
Number of Instances: 307
Number of Input Attributes: 35, Categorical
The values for attributes are encoded numerically
Number of classes : 19
Only the first 15 of which have been used in prior work
(The last 4 classes only have 17 records totally)
Missing Attribute Values: Yes
An unknown value is encoded as ?
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92. WEKA’s Association Mining Module Only Deals with Nominal Attribute
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
63,male,typ_angina,233,no,not_present
67,male,asympt,286,yes,present
67,male,asympt,229,yes,present
38,female,non_anginal,?,no,not_present
...
numeric attribute
nominal attribute
class attribute
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93. WEKA’s Association Mining Module Can’t Be Started with Numeric Attributes
@relation soybean-large-processed
@attribute date {0,1,2,3,4,5,6}
@attribute plant-stand {0,1}
@attribute precip {0,1,2}
@attribute temp {0,1,2}
@attribute hail {0,1}
@attribute crop-hist {0,1,2,3}
@attribute area-damaged {0,1,2,3}
@attribute severity {0,1,2}
………
@data
6,0,2,1,0,1,1,1,0,0,1,1,0,2,2,0,0,0,1,1,3,1,1,1,0,0,0,0,4,0,0,0,0,0,0,diaporthe-stem-canker
4,0,2,1,0,2,0,2,1,1,1,1,0,2,2,0,0,0,1,0,3,1,1,1,0,0,0,0,4,0,0,0,0,0,0,diaporthe-stem-canker
nominal attributes
(encoded numerically)
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95. click
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96. Expand the Window
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97. 2 minimum support = 0.45 minimum confidence = 0.9 Change parameters
item support count
rule confidence
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Default : 10 rules

98. minimum support minimum confidence Set number of rules=15 2018/9/15
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99. click
15 rules
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100. Explorer: attribute selection
Panel that can be used to investigate which (subsets of) attributes are the most predictive ones
Attribute selection methods contain two parts:
A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking
An evaluation method: correlation-based, wrapper, information gain, chi-squared, …
Very flexible: WEKA allows (almost) arbitrary combinations of these two
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108. Explorer: data visualization
Visualization is very useful in practice, e.g. helps to determine difficulty of the learning problem
WEKA can visualize single attributes (1D) and pairs of attributes (2D)
Color-coded class values
“Jitter” option to deal with nominal attributes (and to detect “hidden” data points)
“Zoom-in” function
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109. 1
Load Glass data set 2
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111. 2 1
Change PointSize
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113. 2 1
Change PlotSize
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114. Window size is changed when PlotSize is changed
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115. Double click to enlarge
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118. 1 1 3 2 Choose Rectangle Zoom-in
Click and drag to choose a bolck of data 2
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121. Performing experiments
Experimenter makes it easy to compare the performance of different learning schemes
For classification and regression problems
Results can be written into file or database
Evaluation options: cross-validation, learning curve, hold-out
Can also iterate over different parameter settings
Significance-testing is built in!
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122. Click
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123. Click
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124. 1. Set up parameters 2. Add a few data sets 3. Add a few algorithms
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127. Result message
Running status
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134. The Knowledge Flow GUI New graphical user interface for WEKA
Java-Beans-based interface for setting up and running machine learning experiments
Data sources, classifiers, etc. are beans and can be connected graphically
Data “flows” through components: e.g.,
“data source” -> “filter” -> “classifier” -> “evaluator”
Layouts can be saved and loaded again later
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144. Can continue this...
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155. Conclusion: try it yourself!
WEKA is available at
Also has a list of projects based on WEKA
WEKA contributors:
Abdelaziz Mahoui, Alexander K. Seewald, Ashraf M. Kibriya, Bernhard Pfahringer , Brent Martin, Peter Flach, Eibe Frank ,Gabi Schmidberger ,Ian H. Witten , J. Lindgren, Janice Boughton, Jason Wells, Len Trigg, Lucio de Souza Coelho, Malcolm Ware, Mark Hall ,Remco Bouckaert , Richard Kirkby, Shane Butler, Shane Legg, Stuart Inglis, Sylvain Roy, Tony Voyle, Xin Xu, Yong Wang, Zhihai Wang
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