PMC:2667511 / 25798-26948 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/2667511","sourcedb":"PMC","sourceid":"2667511","source_url":"https://www.ncbi.nlm.nih.gov/pmc/2667511","text":"The performance for different classification methods measured as total accuracy over 10 fold cross validation for ARBC is shown in Table 4. Additionally we performed classification based on the physicochemical properties of the different dom-faces(CWAR), and also ARBC classification based on a rule profile generated using only the set of 58 unique rules discovered (UR). Performance results for these approaches are also given in Table 4. We have seen that in all these cases SVM exhibited the best performance among diverse classifiers studied, reaching over 99% accuracy in some cases. However this high accuracy suggests that overfitting problems are associated with the use of SVM. The other classification approaches evaluated still exhibit a high accuracy with the exception of NB. The performance reached by them is comparable to that previously reported in [12] although not exactly the same instances and features were employed. Additionally we observed that there was no significant appreciable difference between the performance of ARBC and CWAR in most of the situations, although it seems that CWAR performed slightly better than ARBC.","tracks":[{"project":"2_test","denotations":[{"id":"19173748-16423290-8336759","span":{"begin":868,"end":870},"obj":"16423290"}],"attributes":[{"subj":"19173748-16423290-8336759","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#93ecb5","default":true}]}]}}