A Fast Classification Algorithm Using Concept Hierarchy Algorithm

Machine learning deals with programs that learn from experience, i.e. programs that improve or adapt their performance on a certain task or group of tasks over time. The algorithm used for classification is OneR, Naive Bayes and C4.5 algorithm. This work use OneR, it is a simple classification algorithm that generates a one-level decision tree. OneR is able to infer typically simple, yet accurate, classification rules from a set of instances. This paper present Attribute Oriented Induction (AOI) has concept hierarchy as an advantage where concept hierarchy as a background knowledge which can be provided by knowledge engineers or domain experts. The experimental result shows that the proposed method of OneR with Attribute Oriented Induction program provides an accurate result by using UCI repository datasets.