Predictive model based on the evidence theory for assessing critical micelle concentration property

Type de document : Article

Par : Gaudin, Théophile | Samet, Ahmed | Lu, Huiling | Wadouachi, Anne | Pourceau, Gwladys | Van Hecke, Elisabeth | Pezron, Isabelle | El Kirat, Karim | Dao, Tien Tuan

Résumé(s) :

  • In this paper, we introduce an uncertain data mining driven model for knowledge discovery in chemical database. We aim at discovering relationship between molecule characteristics and properties using uncertain data mining tools. In fact, we intend to predict the Critical Micelle Concentration (CMC) property based on a molecule characteristics. To do so, we develop a likelihood-based belief function modelling approach to construct evidential database. Then, a mining process is developed to discover valid association rules. The prediction is performed using association rule fusion technique. Experiments were conducted using a real-world chemical databases. Performance analysis showed a better prediction outcome for our proposed approach in comparison with several literature-based methods.

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