Paper Info
Title
Squeezing the ensemble pruning: faster and more accurate categorization for news portals
Abstract
Recent studies show that ensemble pruning works as effective as traditional ensemble of classifiers (EoC). In this study, we analyze how ensemble pruning can improve text categorization efficiency in time-critical real-life applications such as news portals. The most crucial two phases of text categorization are training classifiers and assigning labels to new documents; but the latter is more important for efficiency of such applications. We conduct experiments on ensemble pruning-based news article categorization to measure its accuracy and time cost. The results show that our heuristics reduce the time cost of the second phase. Also we can make a trade-off between accuracy and time cost to improve both of them with appropriate pruning degrees.
Year
DOI
Venue
2012
10.1007/978-3-642-28997-2_52
ECIR
Keywords
Field
DocType
assigning label,time cost,appropriate pruning degree,ensemble pruning,traditional ensemble,news portal,ensemble pruning-based news article,text categorization,ensemble pruning work,accurate categorization,text categorization efficiency
Data mining,Categorization,Information retrieval,Computer science,Heuristics,Artificial intelligence,Pruning (decision trees),Text categorization,Machine learning,Pruning
Conference
Citations 
PageRank 
References 
2
0.36
2
Authors
2
Name
Order
Citations
PageRank
Cagri Toraman152.03
Fazli Can258194.63