Interval-based attribute evaluation algorithm

Attribute values may be either discrete or continuous. Attribute selection methods for continuous attributes had to be preceded by a discretization method to act properly. The resulted accuracy or correctness has a great dependance on the discretization method. However, this paper proposes an attribute selection and ranking method without introducing such technique. The proposed algorithm depends on a hypothesis that the decrease of the overlapped interval of values for every class label indicates the increase of the importance of such attribute. Such hypothesis were proved by comparing the results of the proposed algorithm to other attribute selection algorithms. The comparison between different attribute selection algorithms is based on the characteristics of relevant and irrelevant attributes and their effect on the classification performance. The results shows that the proposed attribute selection algorithm leads to a better classification performance than other methods. The test is applied on medical data sets that represent a real life continuous data sets.