Feature Subset Selection for Decision Tree Induction in the Context of Otoneurological Data: A Preliminary Study

The definition of representation for learning data is essential, when machine learning methods are applied. Finding of a good subset of attributes [1] may be a tedious task due to wealth of available attributes. In this study, an attribute grouping method based on measures of association and graph theoretic techniques [2] was used to guide the attribute selection for construction of decision trees for six otoneurological diseases.