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Base Oils Biodegradability Prediction with Data Mining Techniques
University of Bizerte, Bizerte, Zarzouna 7021, Tunisia
San Diego Mesa College, 7250 Mesa College Drive, Room K202, San Diego, CA 92111, USA
* Author to whom correspondence should be addressed.
Received: 30 December 2009; in revised form: 24 January 2010 / Accepted: 28 January 2010 / Published: 23 February 2010
Abstract: In 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.
Keywords: base oils; biodegradability; classification models; data mining; multiple linear regression; machine learning models; predictive models
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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.
Abdelmelek SB, Saidane S, Trabelsi M. Base Oils Biodegradability Prediction with Data Mining Techniques. Algorithms. 2010; 3(1):92-99.
Abdelmelek, Sihem Ben; Saidane, Saloua; Trabelsi, Malika. 2010. "Base Oils Biodegradability Prediction with Data Mining Techniques." Algorithms 3, no. 1: 92-99.