Introduction to Weka

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Introduction to Weka

• Xingquan (Hill) Zhu
• Slides copied from Jeffrey Junfeng Pan (UST)

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Outline

• Weka
• Data Source
• Feature selection
• Model building
• Classifier / Cross Validation
• Result visualization

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WEKA

• http//www.cs.waikato.ac.nz/ml/weka/
• Data mining software in Java
• Open source software
• UCI Data Repository
• http//www.ics.uci.edu/mlearn/MLRepository.html

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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, resampling,
  attribute selection, transforming and combining
  attributes,

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WEKA only deals with flat files

• _at_relation heart-disease-simplified
• _at_attribute age numeric
• _at_attribute sex female, male
• _at_attribute chest_pain_type typ_angina, asympt,
  non_anginal, atyp_angina
• _at_attribute cholesterol numeric
• _at_attribute exercise_induced_angina no, yes
• _at_attribute class present, not_present
• _at_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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WEKA only deals with flat files

• _at_relation heart-disease-simplified
• _at_attribute age numeric
• _at_attribute sex female, male
• _at_attribute chest_pain_type typ_angina, asympt,
  non_anginal, atyp_angina
• _at_attribute cholesterol numeric
• _at_attribute exercise_induced_angina no, yes
• _at_attribute class present, not_present
• _at_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
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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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Explorer building classifiers

• Classifiers in WEKA are models for predicting
  nominal or numeric quantities
• Implemented learning schemes include
• Decision trees and lists, instance-based
  classifiers, support vector machines, multi-layer
  perceptrons, logistic regression, Bayes nets,
• Meta-classifiers include
• Bagging, boosting, stacking, error-correcting
  output codes, locally weighted learning,

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Problem with Running Weka
Problem Out of memory for large data set
Solution java -Xmx1000m -jar weka.jar
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Outline

• Weka
• Data Source
• Feature selection
• Model building
• Classifier / Cross Validation
• Result visualization