Hierarchical Classification of Real Life Documents

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Hierarchical Classification of Real Life Documents
Ke Wang, Senqiang Zhou Simon Fraser University
Yu He National University of Singapore
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Hierarchical Multi-classed Documents

• Topics are organized into a hierarchy of
  increasing specificity
• A document is classified into all relevant
  classes.
• For example, a document on Dance could be reached
  from both ArtsPerforming_Arts and Recreation
  topics in Yahoo

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New Issues

• Misclassification is non-symmetric
• Travel ? Outdoor Vs. Travel ? Software
• Documents are multi-classed
• Traditional way only one class attached
• Class space is sparse
• 2 - 1 subsets of classes for k classes
• Exploring the similarities between classes

k
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A New Classification Model

• The model of documents
• t1,t2,.,tnC1,,Ck, where t1,t2,.,tn are
  keywords and C1,,Ck are classes from a given
  class hierarchy
• C1,,Ck is called a classset (CS)
• Construct a classifier
• consisting of rules of the form ti1,, tip ?
  Ci1,, Cip, that assigns a good classset to a
  given new document

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Class Similarity

• Two classsets are similar if they cover similar
  documents.
• Anc(CS) the set of classes in a classset CS plus
  all ancestor classes.
• CS1 is more general than CS2 if Anc(CS1)?
  Anc(CS2)
• Dance is more general than Fast-Dance,Music
  because Anc(Dance) ? Anc(Fast-dance,Music)

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Class Similarity (Cont.)

• A document d is covered by a classset CS if CS is
  more general than the classset of d
• Cover(CS) denotes the set of documents covered by
  CS
• Cover(CS1) ?Cover(CS2)Cover(CS1? CS2)

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Class Similarity (Cont.)

• The dissimilarity of CS1 and CS2 is defined as
  the normalized difference of their coverage
  E(CS1,CS2)
• (Cover(CS2)-Cover(CS1) Cover(CS1)-Cover(CS2)
  )/Cover(CS1) ? Cover(CS2)
• The similarity is defined as 1 - E(CS1,CS2)

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The Confidence

• Match(T?CS ) the set of documents that contain
  all the terms in T.
• The confidence of T?CS is defined as

Match(T?CS ) - ?d E(CSd,CS) Confg(T?CS )
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Match(T?CS )
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Whats behind the Confg ?

• Intuitively, Confg(T?CS ) measures the average
  similarity between CS and the classsets of the
  documents that match T?CS .
• If E(CSd,CS) is binary, i.e., 1 or 0, Confg(T?CS
  ) degenerates to the standard confidence.

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Construction of Classifier

• Step 1 Find association rules
• Generate all association rules of the form T?CS
  that satisfy some user-specified minimum support
  and confidence.

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Construction of Classifier(Cont.)

• Step 2 rank the rules
• A document is classified by the matching rule
  that has highest confidence.
• This selection is called most confidence first
  (MCF)

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Construction of Classifier (Cont.)

• Step 3 remove rules of low accuracy
• Let D be the set of training documents
  classified by rule T?CS, the accuracy of T?CS is
  defined as

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Construction of Classifier (Cont.)

• Confg(T ?CS) is defined with respect to all the
  document ? s that match the rule, whereas
  Accu(T?CS ) is defined w.r.t the documents
  classified by the rule.
• Remove the rules with accuracy below a certain
  threshold because they contribute negatively to
  overall accuracy.

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Construction of Classifier (Cont.)

• Step 4 cut off the ranked list
• If we cut off the list of rules r1,,rm after the
  first i rules, r1,,ri,
• Cutoff error PrefixError(ri)DefualtError(ri)
• PrefixError(ri) is the sum of the rule error
  Error(rj) for all rules rj, 1? j? I
• DefualtError(ri) is the error caused by assigning
  the default classset to all the documents not
  classified by any rule rj

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Experiments
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Experimental Results

• The result on IBM data set
• The error Coverage beats the others.
• The size Confidence gets smaller.
• The time Coverage takes longer.

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Classification Error
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Size Execution Time