MICR Multiple Instance Cluster Regression for Student Academic Performance in Higher Education

Abstract

is an increasing need for the analysis and prediction of the student academic performance in higher education. The ability to predict the student academic performance is also most important in higher education system. There are many challenges in this regard. In this paper, we present a Multiple Instance regression algorithm that models the internal data structure of three data sets to identify items that are most relevant to the data set labels, which operates on three data sets with real valued labels, each containing a set of unlabeled items, in which the relevance of each item to its data set is unknown. The goal is to predict the labels of new data set from its contents. Unlike previous Multiple instance regression methods, Multiple instance cluster Regression can operate on datasets that are structured in which they contain items drawn from a number of distinct unknown distributions. Multiple instance cluster Regression provided predictions that were more accurate than those obtained with non multiple instance approaches or multiple instance regression methods that do not model the data set structures. This Paper makes an attempt towards the application of multiple instance regression algorithms to decide to which category a student belongs (i.e, low#risk students, medium#risk students, high#risk students).