Inductive learning methods for critical decision making applications

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Machine learning is an important research area in computer science. The key activity of machine learning is to extract rules from data sets that can be used to predict results for unseen data accurately. In other words, we try to develop algorithms and techniques that allow computers to learn from data. In this proposal, I will study the theoretical aspects as well as the techniques of granular computing, Self-Organizing Feature Maps (SOFM) and Support Vector Machines (SVM), and apply them to critical decision making problems, such as intrusion detection, marketing analysis, financial prediction, and cancer diagnosis.     As an emerging technique, granular computing has attracted many researchers. The goal of this research is to enhance the learning ability of conventional algorithms as well as to provide more general and flexible granulation algorithms for classification problems and decision making applications.     In SOFM, data are clustered based on a set of neurons with the ability to distinguish themselves in terms of differences between input vectors. The competitive process of determining which neuron best fits a particular input vector is challenging when there are many similarity measures are taken into consideration. Game theory and other techniques will be explored to enhance the competitive process in SOFM and thus give it the ability to better organize and analyze more data characteristics.     SVM has demonstrated excellent performance in real-world applications such as categorization, recognition, and classification. We have adapted rough set theory to SVM for feature selection problems and successfully applied it to an intrusion detection problem. I will continue to explore possible improvements in SVM models for this problem.     Our ultimate goal is to develop a decision support system that will help users determine which learning method is most suitable for different applications.