A New Pairwise Ensemble Approach for Text Classification
Yan Liu
Jaime G. Carbonell
Rong Jin
10.1184/R1/6591107.v1
https://kilthub.cmu.edu/articles/journal_contribution/A_New_Pairwise_Ensemble_Approach_for_Text_Classification/6591107
Text classification, whether by topic or genre, is an important
task that contributes to text extraction, retrieval, summarization
and question answering. In this paper we present a new pairwise ensemble
approach, which uses pairwise Support Vector Machine (SVM) classifiers
as base classifiers and “input-dependent latent variable” method
for model combination. This new approach better captures the characteristics
of genre classification, including its heterogeneous nature. Our
experiments on two multi-genre collections and one topic-based classification
datasets show that the pairwise ensemble method outperforms
both boosting, which has been demonstrated as a powerful ensemble
approach, and Error-Correcting Output Codes (ECOC), which applies
pairwise-like classifiers for multiclass classification problems.
2003-10-01 00:00:00
computer sciences