A novel discretization technique using Class Attribute Interval Average
Abdulloh Baka, Wiphada Wettayaprasit, Sirirut Vanichayobon · 2014
Discretization algorithm is important for data mining preprocessing because it will help the user to easily understand the data, reduce the complexity of data, reduce processing time, and increase efficiency and accuracy of the data. This paper proposes the new discretization algorithm called Class Attribute Interval Average (CAIA). The algorithm uses 2D-quanta matrix table to calculate each of class individual interval's average and merge the best adjacent intervals to form the new interval. The experimental design uses four-UCI data sets (Iris, Breast Cancer, Heart Diseases, Glass) and four-classification algorithms (J48, RBF, MLP, NB). The comparisons of experimental result with the other six discretization algorithms (EW, EF, ChiMerge, IEM, CAIM, CACC) show that the proposed CAIA has the best mean rank for both of the accuracy and the number of intervals.