Meta-classifier approach to reliable text classification(3)

A problem with automatic classifiers is that there is no way to know if a particular classification is just a guess or a certain answer. Reliable classification is the task of predicting whether a certain instance is correctly classified or not, i.e., a cl

Ourthirdresearchquestionis:

3.Shouldthemeta-classi erbelocalorglobal?

Inadditiontotheglobalapproach,thatlearnsonemeta-classi erforallclasses,

alocalapproachisapplied.Thelocalapproachlearnsonemeta-classi erfor

eachclassintheoriginaldata.Ourstudyshowstheglobalapproachisprefer-

able,itachievesthesameperformancewithlesscomputationale ort.

Themeta-classi erapproachprovidesasoundsolutiontothetaskofreliable

classi cation,iftheprecisionofthemeta-classi eronthereliableclassis100%.

Inpracticewedonotreachthislevelofaccuracy.

Ourconclusion,toanswertheproblemstatement,isthatthemeta-classi er

approachinpracticedoesnotprovideasoundsolutiontoreliabletextclas-

si cation.Themeta-classi erapproachdoesachieveaconsiderableincrease

inaccuracy.Itisane cientsolutionaslongasthebaseclassi ersandthe

meta-classi ersaree cient,andthepropermetadatarepresentationisused.

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