Paper Info
Title
Improving Accuracy in the Montgomery County Corrections Program Using Case-Based Reasoning
Abstract
The Montgomery county corrections program is a program designed to address the problem of overcrowded jails by providing an out-of-jail rehabilitative program as an alternative. The candidate offenders chosen for this program are offenders convicted on nonviolent charges and are currently chosen subjectively with little statistical basis. In addition, historical data has been recorded on offenders who have passed through the program, making the program a good candidate for case-based reasoning. Using such reasoning, county officials would like an objective measurement which will predict the success or failure of a candidate offender based on past offender history. The four case-based reasoning algorithms chosen for this prediction are discrete, continuous and distance weighted k-nearest neighbors and a general regression neural network (GRNN). Although all four algorithms prove to be an improvement on the current system, the GRNN performs the best, with an average accuracy rate of 68%.
Year
DOI
Venue
2008
10.1109/ICMLA.2008.94
San Diego, CA
Keywords
Field
DocType
time-series segmentation,bayesian approach,improving accuracy,unsupervised scenario,linear gaussian,fundamental problem,montgomery county corrections program,segmentation model,case-based reasoning,case based reasoning,neural nets,regression analysis,classification algorithms,program design,machine learning,accuracy,use case,cognition,prediction algorithms,k nearest neighbor,case base reasoning
Distance measurement,General regression neural network,Regression analysis,Computer science,Prediction algorithms,Artificial intelligence,Case-based reasoning,Artificial neural network,Statistical classification,Machine learning,Law administration
Conference
ISBN
Citations 
PageRank 
978-0-7695-3495-4
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Caio Soares1112.61
Christin Hamilton220.72
Lacey Montgomery3101.60
Juan E. Gilbert417044.51