Determining the Optimal Parameter for Education Surveillance System
Sageemas Na Wichian, Suwimon Wongwanich, Patharawut Saengsiri · Advanced materials research · 2011
Education Surveillance System is designed for predicting the state of education based on form of alarm signal using Incremental Leaning based on Mahalanobis Distance (ILM). However, ILM need to define two crucial parameters (co-variance matrix and distance threshold) it is not only very difficult for determining by general user but also depend on dataset property. This research proposed GAILM algorithm based on Ordinary National Education Test (Bangkok) dataset for finding approximate parameter and predicting. The result of experiment is represent GAILM technique discovering proximate co-variance matrix (0.91) and distance threshold parameter (0.44) and also high accuracy rate as 90.91% and 92.07%, in the year 2007 to 2008 respectively. This result was higher than the accuracy rate of traditional technique by K-Means algorithm and Cobweb.