The hybrid model uses data mining to improve accuracy and uses genetic algorithm and evolutionary strategy to evolve the knowledge base of FES (DEFES).
To identify minimal number (m) out of 531 PCP features while establishing an SVM-based training classifier (referred to as PCP mining) with maximal accuracy is a bi-objective combinatorial optimization problem [ 43].
The Random Forest (RF) algorithm [ 8] has become very popular for pattern recognition in omics-scale data, mainly because RF provides two aspects that are very important for data mining: high prediction accuracy and information on variable importance for classification.
Depending on the used imaging method and the performed data mining technique, classification accuracies between 90.3% and 97.3% were achieved, only taking values concerning the differentiation between different tumor types into account.
The high capital investment in mining projects requires more accuracy in preparing base data.
In general, for accuracy of mining subsidence the relative error m d was used: m_{d} = pm frac{m}{{W_{hbox{max} } }} times 100;%,where W max is the maximum subsidence in the field data.
The comparison of performance on label-based EQs generated by biocurators using Phenex and PCS shows that the text mining tool improved curation accuracy for two of the three biocurators (compare Phenex and Phenex+Charaparser results in Table 4).
ELM uses context-based filtering and text mining to improve the accuracy of assigned pattern-matching probabilities based on Mode I and Mode II 14-3-3-binding as well as non-consensus 14-3-3-binding 14-3-3-binding 14-3-3-binding 14-3-3-binding
For each classification algorithm, we report its predictive accuracy when mining different datasets produced with three different values of the parameter "GO term occurrence threshold", which specifies the minimum number of occurrences in the dataset for a GO term to be used as an attribute (see Methods), namely 3, 7 and 11.
