Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools improve students’ grades retention. Prediction performance a difficult process owing massive quantity educational data. Therefore, Artificial Intelligence (AI) techniques can be used for data mini...

One of the major challenges when working with software metrics datasets is that some metrics may be redundant or irrelevant to software defect prediction. This may be addressed using feature (metric) selection, which chooses an appropriate subset of features for use in downstream computation. There are three major forms of feature selection: filter-based feature rankers, which uses statistical ...

Abstract: Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computati...

Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...

In this paper we cover some reference paper and compared different algorithm on the basis of their performance and selection of data set. Where the efficiency concerns on the time evaluation of features selection, and the effectiveness is related to the quality of the subset of features selection. We analysis this report based on feature subset selection algorithm from the years of 1997 to 2013...

Feature subset selection is of great importance in the field of data mining. The high dimension data makes testing and training of general classification methods difficult. In the present paper two filters approaches namely Gain ratio and Correlation based feature selection have been used to illustrate the significance of feature subset selection for classifying Pima Indian diabetic database (P...