ML: Lecture 4

Readings for the quiz

• in Witten: Chapters 1 and 2 and section 11.3
• The types of concepts: classification, clustering, associations,
• types of data: nominal, numeric, ordinal
• examples: sister-of, contact lenses, weather, irises, soybean classification, etc
• ethics: why do we need to think about it?

Additional resources

• Computer Vision by Shapiro and Stockman
• Image processing by Steve Tanimoto (probably too elementary)

Attributes and Data

• What kinds of data are there in Weka?
• Why data needs to be cleaned/preprocessed: missing values, inconsistent values
• Summarizing data: mean, standard deviation, min, max, quartiles
• goal: finding a minimum set of attributes that adequately describes the concept.
• What is the syntax for ARFF format?
  

  
  
    @relation weather
  
    @attribute outlook {sunny, overcast, rainy}
  
    @attribute temperature numeric
  
    @data
  
   How does ARFF denote string data?
   How does ARFF handle multi-instance data?
   What does the following mean?
    @attribute bag relational
        @attribute outlook  {sunny, overcast, rainy}
        @attribute temperature numeric
    @end bag
    @attribute play? {yes, no}
    
   attribute subset selection: finding a minimum set of attributes that adequately describes the concept.
  

Filtering data

• A filter can be almost any function that transforms the input
• There are different types of filters: supervised and unsupervised, attribute filters,
  instance filters.

• Examples:

     AddCluster: adds a new nominal attribute which is an ID of each cluster
     AddNoise: flips some of the values in the input for a nominal attribute 
     NominalToBinary (supervised in the case of numeric values)
     Randomize(unsupervised, instance)
     Resample(unsupervised, instance)
     SpeadSubSample (supervised, instance)