Name
Title | ||
|---|---|---|
A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.asoc.2011.11.001 | Appl. Soft Comput. |
Keywords | Field | DocType |
different real data set,conjugate gradient training algorithm,new discretization algorithm,bottom-up discretization algorithm,data range,better discretization scheme,neural networks classifier,smallest discretization time,discretization scheme,discretization process,range coefficient,proposed algorithm,discretization algorithm show,preprocessing,classification,data mining | Conjugate gradient method,Discretization,Data set,Artificial intelligence,Artificial neural network,Index of dispersion,Feedforward neural network,Mathematical optimization,Skewness,Algorithm,Machine learning,Mathematics,Discretization of continuous features | Journal |
Volume | Issue | ISSN |
12 | 2 | 1568-4946 |
Citations | PageRank | References |
8 | 0.42 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Gethsiyal Augasta | 1 | 56 | 3.01 |
| T. Kathirvalavakumar | 2 | 88 | 5.75 |