MATLAB code of the following paper is ready for download :
Augasta, M. Gethsiyal, and T. Kathirvalavakumar. “A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier.” Applied Soft Computing 12.2 (2012): 619-625.
This Code is with two example on two dataset.
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.
A newly developed discretization method is presented. It discretizes the continuous attributes based on range coefficient of dispersion and skewness of data and hence the proposed method is discretization based on range coefficient of dispersion and skewness of data (DRDS).
The quality of the discretization is measured by two parameters, namely classification accuracy and number of discretization intervals . More discretization intervals always fewer the classification
errors and lower the cost of data discretization . The DRDS method has two phases. The first phase interested only in minimizing the classification errors, resulting more intervals in initial discretization scheme (IDS) and the second phase interested in minimizing the number of intervals without affecting the classification accuracy by merging the intervals in IDS. From the phase 2, the final discretization scheme (FDS) is obtained.
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