Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the way features influence the classification. In areas such as health informatics a classifier that clearly identifes the influences on classifcation can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifer that provides accuracy comparable to other techniques whist providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classifcation and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.
|Number of pages||16|
|Journal||International Journal of Software Informatics|
|Publication status||Published - Dec 2008|