Building Hierarchical Classifiers Using Class Proximity

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Building Hierarchical Classifiers Using Class
Proximity

• Ke Wang
• Senqiang Zhou
• Shiang Chen Liew
• National University of Singapore

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Hierarchical classification

• Given
• a class hierarchy
• a collection of pre-classified documents
• a document is a set of terms
• Build
• a classifier that assigns a relevant class to a
  new document
• Key
• extract features of classes

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Yahoo classes
Yahoo
recreation
science
sports
automotive
cycling
skating
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ACM classes
Hardware
Level 1
General
Memory_structure
Level 2
Level 3
Design_style
General
Cache_memories
Level 4
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Existing local approaches

• build one classifier at each split of the class
  hierarchy
• determine features locally at each node
• classify a document by going through a path of
  classifiers starting from the root

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Diminishing of high level structure

• rely on classification at high levels
• but high level structures usually weak, i.e.,
  divergence of topics
• e.g., car is a feature at Recreation
  Automotive, but not at Recreation

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Bias of misclassification

• sibling classes Vs. nephew classes
• misclassification at high levels Vs. at low
  levels
• specialisation Vs. generalisation

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Features should be

• determined wrt the target class
• determined at all concept levels
• correlated
• The solution generalised association rules
  (SA95, HF95)
• sql, IO ? DB
• language, performance ? CS

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Our approach

• class proximity
• global classifier
• term hierarchy
• use the best generalised association rule T? C
  to determine the class

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Rank association rules

• Biased confidence

• Biased J-measure

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An example
Arts
Music
Literature
...
...
A_Music
A_Literature
author
story
Class hierarchy
Term hierarchy
editor
writer
poem
fiction
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Term hierarchy(T)YesClass proximity(B)Yes

• R0 author,story?Literature (ConfB1,Clistd6,d7)
  
• R1 author?Literature (ConfB1)
• R2 story?Literature (ConfB0.67, Wlistd5(1))
• R4 hall?Music (ConfB0.4, Clistd1,d2,
  Wlistd3(1))
• R3 States?A_Literature (ConfB0.33, Clistd4,d5)

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Experiment I

• http//www.acm.org/dl/toc.html/
• 26,515 papers, 78 classes, 14,754 terms
• class hierarchyLevel-1 and level-2 categories
• term hierarchyLevel-3 and level-4 categories
• documentTitle and level-4 categories

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Best rules found by (B,T)

• CSO
• vector,stream,processor,parallel?Processor_Archite
  ctures
• multiple_instruction_stream?Processor_Architecture
  s
• data_flow,architectur?Processor_Architectures
• internet, architectur?Computer_Communication_Netwo
  rks
• mode,atm?Computer_Communication_Networks
• network,circuit_switching? Computer_Communication_
  Networks
• tecniqu, model, attribut?Performance_of_Systems
• Software
• program,function, application?Programming_Techniqu
  es
• object_oriented_programming?Programming_Techniques
  
• reusable_software?Software_Engineering
• software,methodologie?Software_Engineering
• organization, distributed_system?Operating_Systems
  

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() --- (T) --- ?
(B) --- ? (B,T) --- ?
(CDAR97,T) --- ?? (CDAR97) --- ?
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Experiment II

• http//dir.yahoo.com/recreation/sports
• 7,550 documents
• 367 classes, 7 levels
• 10,747 terms
• 90 of the terms occur in no more than 10
  documents and many documents contain only such
  terms

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Best rules found by (B,T)

• SportsCycling
• page,mountain ?Mountain_Biking
• product,bike?Mountain_Biking
• mtb,mountain ?Mountain_Biking
• held,bicycl?Races
• classic,bicycl?Races
• trip,tour?Travelogues
• trip,canada ?Travelogues
• bicycl,alaska?Travelogues
• SportsAuto_Racing
• team,result,driver?Formula_one
• model,featur?Tracks_and_Speedways
• oval?Tracks_and_Speedways
• raceway?Tracks_and_Speedways

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