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Base Oils Biodegradability Prediction with Data Mining Techniques
AbstractIn this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques.
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MDPI and ACS Style
Abdelmelek, S.B.; Saidane, S.; Trabelsi, M. Base Oils Biodegradability Prediction with Data Mining Techniques. Algorithms 2010, 3, 92-99.View more citation formats
Abdelmelek SB, Saidane S, Trabelsi M. Base Oils Biodegradability Prediction with Data Mining Techniques. Algorithms. 2010; 3(1):92-99.Chicago/Turabian Style
Abdelmelek, Sihem Ben; Saidane, Saloua; Trabelsi, Malika. 2010. "Base Oils Biodegradability Prediction with Data Mining Techniques." Algorithms 3, no. 1: 92-99.