CONSTRUCTION OF META CLASSIFIERS FOR ACADEMIC RESEARCH DATA FROM SOCIAL NETWORKS

Study of research progress in the academic domain is challenging for research communities and funding agencies. The data recovered from the social networks augment this issue for supporting the results in this direction. Here in this paper we address this issue positively with the help text mining tasks. Classification as one of the major data mining methodologies can be applied effectively for this purpose. The objective of this paper is to check the learning algorithms for classification such examples based on selected dataset for research articles in technical conferences. The main intention in this context is to deal with available data set for high accuracy. For this purpose AdaboostM1, Bagging, Dagging, OrdinalClass Classifiers, Stacking models are built using an open source mining Weka under supervised learning algorithms. It is necessary to reduce the error before constructing the final models and thus the varying the parameters and number of iterations for training is carried out.