Abstract
One of the approaches in the Knowledge Discovery in Databases (KDD) domain is Predictive Toxicology (PT). Its aim is to discover and represent the relationships between the chemical structure of chemical compounds and biological and toxicological processes. The challenges in real toxicology problems are big amount of the chemical descriptors and imperfect data (means noisy, redundant, incomplete, and irrelevant). The main goals in knowledge discovery field are to detect these undesirable proprieties and to eliminate or correct them. This supposes noise reduction, data cleaning and feature selection because the performance of the applied Machine Learning algorithms is strongly related with the quality of the used data. In this paper, we present some of the issues that can be performed for preparing data before the knowledge discovery process begin.
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Cocu, A., Dumitriu, L., Craciun, M., Segal, C. (2008). A Hybrid Approach for Data Preprocessing in the QSAR Problem. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_72
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DOI: https://doi.org/10.1007/978-3-540-85563-7_72
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