Supervised Classification Using Balanced Training

Mian Du, Matthew Pierce, Lidia Pivovarova, Roman Yangarber

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

We examine supervised learning for multi-class, multi-label text
classification. We are interested in exploring classification in a
real-world setting, where the distribution of labels may change
dynamically over time. First, we compare the performance of an array of
binary classifiers trained on the label distribution found in the
original corpus against classifiers trained on balanced data, where
we try to make the label distribution as nearly uniform as possible. We
discuss the performance trade-offs between balanced vs. unbalanced
training, and highlight the advantages of balancing the training set.
Second, we compare the performance of two classifiers, Naive Bayes and
SVM, with several feature-selection methods, using balanced training. We
combine a Named-Entity-based rote classifier with the statistical
classifiers to obtain better performance than either method alone.
Alkuperäiskielienglanti
OtsikkoUnknown host publication
Sivumäärä12
KustantajaSpringer-Verlag
Julkaisupäivälokakuuta 2014
TilaJulkaistu - lokakuuta 2014
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Statistical Language and Speech Processing (SLSP 2014) - Grenoble, Ranska
Kesto: 14 lokakuuta 201416 lokakuuta 2014
Konferenssinumero: 2

Julkaisusarja

NimiLecture notes in artificial intelligence
Numero8791

Tieteenalat

  • 113 Tietojenkäsittely- ja informaatiotieteet

Siteeraa tätä

Du, M., Pierce, M., Pivovarova, L., & Yangarber, R. (2014). Supervised Classification Using Balanced Training. teoksessa Unknown host publication (Lecture notes in artificial intelligence; Nro 8791). Springer-Verlag.