The role of data reduction for diagnosis of pathologies of the vertebral column by using supervised learning algorithms

Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling these large databases often too big to be processed. Data reduction techniques are therefore a very important step to prepare the data before data mining and knowledge discovery. In this paper we present a comparative study on original and reduced data to see the role data reduction in a learning task. For this purpose, we used a medical dataset; especially a vertebral column pathologies database.