Analysis of high-throughput data by means of support vector machines and kernel-based techniques: feature selection and adaptive model building.

In many real-life applications, information gathered from measurements is essential to ensure the quality of products and to enable control of a production process. These measurements are typically obtained from online hardware analysers (e.g. thermometers, flow meters, etc). However, there are many characteristics that cannot be obtained through online equipment and for which time-consuming and computationally expensive analysis is required.For this reason models are typically used to predict the results of such an analysis from the process variables. The analysis is then used as a confirmation of the model. Models are sometimes also used to predict online hardware analysers. Online analysers may fail due to corrosion or drift from their calibration point.In this project we address a number of issues related to the construction of models using Support Vector Machines. Our interest in building models using SVMs has several reasons.- It is well-known that SVMs can handle high-dimensional data without suffering from the curse of dimensionality. - The use of kernels enables nonlinear modelling.- SVMs can be made insensitive to noise and outliers.- Finally, the ability of SVMs to identify "unusual" data points makes it useful in detecting outliers and anomalies.The issues we aim to address in this project are the following.I. Feature selection and incorporation of prior knowledgeIt is the aim to investigate whether similar results can be obtained for Support Vector Regression and how well the technique applies to single-class problems.II. Adaptive model buildingTechniques that can handle the adaptivity of the inferential sensor at all levels, and especially when the mathematicalmodel needs to be partially rebuilt, are still in their infancy and are the second topic of this research project.

Keywords:ARTIFICIAL INTELLIGENCE, INFORMATICS, MODEL BUILDING

Disciplines:Applied mathematics in specific fields, Artificial intelligence, Cognitive science and intelligent systems