TY - GEN
T1 - Grouped ECOC conditional random fields for prediction of web user behavior
AU - Guo, Yong Zhen
AU - Ramamohanarao, Kotagiri
AU - Park, Laurence A.F.
PY - 2009
Y1 - 2009
N2 - Web page prefetching has shown to provide reduction inWeb access latency, but is highly dependent on the accuracy of the Web page prediction method. Conditional Random Fields (CRFs) with Error Correcting Output Coding (ECOC) have shown to provide highly accurate and efficient Web page prediction on large-size websites. However, the limited class information provided to the binary-label sub-CRFs in ECOC-CRFs will also lead to inferior accuracy when compared to the single multi-label CRFs. Although increasing the minimum Hamming distance of the ECOC matrix can help to improve the accuracy of ECOC-CRFs, it is still not an ideal method. In this paper, we introduce the grouped ECOC-CRFs that allow us to obtain a prediction accuracy closer to that of single multi-label CRFs by grouping the binary ECOC vectors. We show in our experiments that by using the grouping method, we can maintain the efficiency of the ECOC-CRFs while providing significant increase in Web page prediction accuracy over ECOC-CRFs.
AB - Web page prefetching has shown to provide reduction inWeb access latency, but is highly dependent on the accuracy of the Web page prediction method. Conditional Random Fields (CRFs) with Error Correcting Output Coding (ECOC) have shown to provide highly accurate and efficient Web page prediction on large-size websites. However, the limited class information provided to the binary-label sub-CRFs in ECOC-CRFs will also lead to inferior accuracy when compared to the single multi-label CRFs. Although increasing the minimum Hamming distance of the ECOC matrix can help to improve the accuracy of ECOC-CRFs, it is still not an ideal method. In this paper, we introduce the grouped ECOC-CRFs that allow us to obtain a prediction accuracy closer to that of single multi-label CRFs by grouping the binary ECOC vectors. We show in our experiments that by using the grouping method, we can maintain the efficiency of the ECOC-CRFs while providing significant increase in Web page prediction accuracy over ECOC-CRFs.
KW - Conditional random fields
KW - Error correcting output coding
KW - Grouping
KW - Web page prediction
UR - http://www.scopus.com/inward/record.url?scp=67650702500&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-01307-2_77
DO - 10.1007/978-3-642-01307-2_77
M3 - Conference Paper
AN - SCOPUS:67650702500
SN - 3642013066
SN - 9783642013065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 757
EP - 763
BT - 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
T2 - 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Y2 - 27 April 2009 through 30 April 2009
ER -