Abstract
Research project selection is an essential task for government and private agencies. When a huge number of research proposals are received, it is common to group them along with their similarities in research discipline areas. Then the grouped proposals are assigned to the appropriate experts for peer review. Existing approaches are not efficient to classify the document of project proposal correctly. Text-mining methods are used to solve the problem of classifying text documents automatically. In this paper, research proposals are classified based on the discipline areas and proposals in each discipline are grouped using the text-mining technique. Dice’s coefficient, Damerau–Levenshtein distance, Tversky index, Cosine similarity and Jaro–Winkler distance are used to find the similarity between the documents. The classification is used to predict the target class for each document proposal in the dataset accurately. In this work, Proposed Ensemble classifiers are used that contain various classifiers such as Kernel Support Vector Machine (KSVM), Self-Organizing Map (SOM), K-Nearest Neighbor (KNN) and Naïve Bayes. These are the four classification methods used for proposed work and get an accuracy of results.
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Saravanan, R.A., Rajesh Babu, M. Enhanced text mining approach based on ontology for clustering research project selection. J Ambient Intell Human Comput (2017). https://doi.org/10.1007/s12652-017-0637-7
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DOI: https://doi.org/10.1007/s12652-017-0637-7