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Multiclass Classification

  • Classification involving more than two classes (i.e., > 2 Classes)
  • Method 1. One-vs.-all (OVA): Learn a classifier one at a time
    • Given m classes, train m classifiers: one for each class
    • Classifier j: treat tuples in class j as positive & all others as negative
    • To classify a tuple X, the set of classifiers vote as an ensemble
  • Method 2. All-vs.-all (AVA): Learn a classifier for each pair of classes
    • Given m classes, construct m(m-1)/2 binary classifiers
    • A classifier is trained using tuples of the two classes
    • To classify a tuple X, each classifier votes. X is assigned to the class with maximal vote
  • Comparison
    • All-vs.-all tends to be superior to one-vs.-all
    • Problem: Binary classifier is sensitive to errors, and errors affect vote count

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