2026-09-19T05:58:09Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81543 2023-07-11 col_2142_5131 col_2142_8951 com_2142_5130 com_2142_8950 com_2142_150

Linda Smith Yu, Bei 2015-09-25T20:17:10Z 2015-09-25T20:17:10Z 10000-01-01 2006 2006 Some of our conclusions are consistent with what are obtained in topic classification, such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this area---SVM and naive Bayes select top features in different frequency ranges; stemming might harm feature selection methods. These experiment results provide new insights to the relation between classification methods, feature engineering options and non-topic document properties. They also provide guidance for classification method selection in literary text classification applications. Made available in DSpace on 2015-09-25T20:17:10Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3250350.pdf: 2849282 bytes, checksum: 4b8f4f71692b2809a97621757dacd4cf (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 82824 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 116 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/81543 (MiAaPQ)AAI3250350 eng Literature, American An Evaluation of Text Classification Methods for Literary Study text Library and Information Science Library and Information Science University of Illinois at Urbana-Champaign Dissertation Ph.D.