An Algorithm for Discretization of Continuous Type of Attributes which Keeps Classification Property Invariant
Beilei Xiao · Journal of Qufu Normal University · 2005
Discretization of continuous type of attributes is a key issue in machine learning, it is a NP puzzle. This paper, aiming at decision tables, presents an effective and easily understandable discretization method based on Naive Scaler algorithm. it considers simultaneously, not separately, all attributes in decision tables, consequently, the break-points obtained are much less than that obtained by Naive Scaler algorithm, while the original classification property of the decision table keeps invariant.