A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier

In this paper we propose a new static, global, supervised, incremental and bottom-up discretization algorithm based on coefficient of dispersion and skewness of data range. It automates the discretization process by introducing the number of intervals and stopping criterion. The results obtained using this discretization algorithm show that the discretization scheme generated by the algorithm almost has minimum number of intervals and requires smallest discretization time. The feedforward neural network with conjugate gradient training algorithm is used to compute the accuracy of classification from the data discretized by this algorithm. The efficiency of the proposed algorithm is shown in terms of better discretization scheme and better accuracy of classification by implementing it on six different real data sets.

► Proposed a new static, global, supervised, incremental and bottom-up discretization algorithm (DRDS) based on coefficient of dispersion and skewness of data range. ► DRDS automates the discretization process by introducing the number of intervals and stopping criterion. ► The results obtained using this discretization algorithm show that the discretization scheme generated by the algorithm almost has minimum number of intervals and requires smallest discretization time. ► The feedforward neural network with conjugate gradient training algorithm is used to compute the accuracy of classification from the data discretized by this algorithm. ► Requires minimum discretization time as sorting procedure is used only for S tuples, where S is number of target classes.