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IMPROVING STRUCTURE BASED MODELS FOR PREDICTING CHEMICAL FUNCTIONS AND WEIGHT FRACTIONS IN COSMETIC PRODUCTS USING ENSEMBLE SUPPORT VECTOR MACHINE

TANJA VOJINOVIC 1*, LJILJANA KASCELAN 2, ZORICA POTPARA 1, NATASA RADONJIC 1, ZORKA KASCELAN 1

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Through usage of a large number of cosmetic products, consumers are very often exposed to toxic chemicals. This paper aimed to propose a model for the prediction of chemical functions and weight fractions in these products based on the structural and physico-chemical properties of the substances. Due to the imbalance of classes we used Support Vector Machine (SVM) method, which can complement a smaller class with the examples that are most similar to it and identify the examples that are most different. The generality of the SVM method was additionally enhanced by combining it with ensemble Bootstrap Aggregation (Bagging). The research results showed that the proposed bagging SVM method can overcome the disadvantages of previously applied methods. Furthermore, it can help address the lack of information needed to assess exposure to risk from the use of cosmetic products containing toxic chemicals in their composition. The proposed models can be applied to predict whether a certain chemical may be a substitute for a function performed by another possibly toxic chemical in a cosmetic product, as well as to determine the quantity proportion of a certain dangerous chemical on the basis of its chemical structure and physico-chemical properties.