Although kernel methods can provide enhanced classification results, they usually suffer from high computational complexity.
Zhang et al. [22] adopted domain feature enhanced classification model for the detection of Chinese phishing e- business websites.
Results indicate that glottal signal features provide good separation of spoken emotions and achieve enhanced classification performance when compared to other approaches.
Tau phosphorylated at threonine 231 (p-tau231) differentiated between AD and frontotemporal dementia, tau phosphorylated at serine 181 (p-tau181) enhanced classification between AD and dementia with Lewy bodies.
Thus our data shows enhanced classification if analysis is performed with multiple cytokines.
The proposed measure can be also used to derive new features that enhance classification accuracy.
In particular, the proposed approach helps to increase the classification accuracy, while enhancing classification efficiency by requiring considerably less features.
In designing fuzzy rule based classification systems (FRBCSs), complex fuzzy rule extraction techniques and tuning membership functions are frequently used to enhance classification accuracy.
Also there is no comparison evaluation to determine if using these Stylometric features in addition to n-gram features enhances classification performance.
Moreover, we demonstrated how this technique can enhance classification accuracy while making the neural network unbiased in terms of view point.
