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Title:
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USING SELECTION ATTRIBUTE ALGORITHMS FROM DATA MINING TO COMPLEMENT THE REP-INDEX |
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Author(s):
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Gláucio R. Vivian, Cristiano R. Cervi, Diogo N. Rovadosky |
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ISBN:
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978-989-8533-57-9 |
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Editors:
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Pedro Isaías |
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Year:
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2016 |
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Edition:
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Single |
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Keywords:
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Data Mining, Researchers Reputation, Rep-Index, Selection Algorithms, Scientometrics |
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Type:
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Full Paper |
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First Page:
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219 |
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Last Page:
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226 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In this paper we propose a complement to the Rep-Index reputation metric for researchers, using data mining techniques specific for selecting a subset of the most relevant elements to Rep-Index. We redid the experiments with the original authors data and found the best algorithm. At the end we propose the new weights for classification of researchers with correct classification rate higher than the original Rep-Index. For Computer Science the best selection attribute is InfoGain. The Spearmans correlation is 0.5520 against 0.3932 for Rep-Index with original weights. The results show that Rep-Index is adaptable, flexible and the approach proposal complements the original index to offer better classification for researchers. |
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