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See:
Description
Overview for extended WEKA including Ensembles of Hierarchically Nested Dichotomies.
The extensions for EHNDs are based on WEKA version 3-4-1 (March 2004) and the classes
weka.classifiers.meta.END
and
weka.classifiers.meta.ND
as provided by Eibe Frank.
weka.classifiers.meta.NestedDichotomy
weka.classifiers.meta.HND
weka.core.ClassHierarchy
weka.core.ClassTree
weka.core.converters.ClassHierarchyParser
weka.core.converters.AbstractClassHierarchyParser
weka.core.converters.ClassTreeParser
weka.core.converters.ClassTreeFileParser
weka.core.converters.ClassTreeArffFileParser
weka.core.converters.HierarchicalCostMatrix
weka.classifiers.meta.END
:
weka.classifiers.meta.NestedDichotomy
as base learner instead of weka.classifiers.meta.ND
.setHashtable()
in preparation of base classifier.weka.classifiers.meta.ND
: Implements marker-interface weka.classifiers.meta.NestedDichotomy
.weka.core.Instances
:
lastRemoved
to keep track of last removed attribute-position.classRemoved
to provide whether the class-attribute was removed. To be set to false, if the class-attribute was added again or another attribute was deleted afterwards.deleteAttributeAt(int position)
: keeps track of the currently deleted position and whether the class-attribute was removed.insertAttributeAt(Attribute att, int position)
:
If the class-attribute was removed and position
equals lastRemoved
,
m_ClassIndex
is set to position and classRemoved
is set to false
.
Currently neither END, nor ND or HND are enabled to run with graphical user interface. For usage of EHNDs in command-line interface call
weka.classifiers.meta.END
with the respective parameters, weka.classifiers.meta.HND
as base learner.
In theory HND
provides the means to run a hierarchical classification with any base classifier.
We propose to generalize the class to handle any base learner, and to incorporate it as an independent meta schema.
Currently the hierarchy can be encoded in an arff file as comment. Thus the hierarchy will get lost if a respective new file is created after filtering. Perhaps it could be possible to incorporate a new (optional) key-word to the arff-format denoting a line that provides an encoded hierarchy.
Furthermore, by omitting the call of setHashtable()
, END become a merely arbitrary ensemble method,
that is not realy specialized to run with ND or HND as base learner only.
Thus the restriction by requiring a base learner implementing the marker interface
weka.classifiers.meta.NestedDichotomy
could eventually be removed.
The same effect, however, could perhaps be achieved using
RandomCommittee
, so END might become somewhat redundant.
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