Application of Multilayer Feed Forward Artificial Neural Network Perception in Prediction of Court Case's Time Span: The Case of Federal Supreme Courts
No Thumbnail Available
Date
2009-01
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
This research examines and analyzes the use and application of neural networks as a predictive
tool. The research was undergone with the assumption to give the Federal Supreme courts in
advance estimation of the court case's time span. The significance of the research could possibly
benefit a plaintiff and defendants to know their case time length in prior as well the federal courts
to perform court room monitoring, ensuring transparency and work efficiency. A model to address these needs was constructed using a feed forward multiplayer neural network
perception having 9 input neurons to the network and one hidden layer with 20 neurons and
finally having a single output neuron, which is the predicted time of the cases in months using
MA TLAB 7.0 neural network tool box. A selected model was trained with training and validation
datasets[67% of the whole datasets], finally tests with the test set reserved for these
purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean
Square error [MSE] which is the difference between the target output and the network output was
minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was
between ±O.2 normalized months. This is the good indi cation that the developed model could be
a reliable predictive model for court cases time span especially for criminal, civil and labor court
cases with the assumption that the external factor that affect the court case time span prediction
are constant and stable. Finally when the network is trained with same court case types, the network has show high
predictive capability for criminal cases with 95.65% of the data sets residual error minimized
between ±...O.005, 89.54% for civil cases and 91.55% for labor cases. This is the good indication
that the developed predictive model can satisfactorily be an alternate choice for predicting court
case time span especially court cases related to criminal cases.
Description
Keywords
Information Science