Combining the Best Techniques of two Worlds
The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms.
Together with S. J. Ovaska (Espoo/Helsinki, Finland) we introduced a multi-dimensional categorization scheme for fusion techniques that facilitates the discussion of advantages or drawbacks of certain fusion approaches and supports the development of novel fusion techniques and applications. In our lab, we also developed various kinds of fusion techniques, such as
“The Wisdom of Crowds”
“The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations” is a book written by James Surowiecki about the aggregation of information in groups, resulting in better decisions. We aim at adopting this idea in the field of Knowledge Discovery (KD) and Data Mining (DM).
Experts have important qualitative knowledge about interrelations between more or less abstract concepts in an application area. However, the knowledge of a single expert is typically quite uncertain (e.g., incomplete or imprecise). By fusing the knowledge of several experts it would be possible to obtain more certain and, therefore, more valuable knowledge. Conventional systems for KD and DM have the ability to extract valid rules from huge data sets. These rules describe dependencies between attributes and classes in a quantitative way, for instance. By fusing this kind of knowledge with the combined, qualitative knowledge of several experts it would be possible to obtain more comprehensive knowledge about an application area.
We proposed a concept for a new KD & DM technique based on Computational Intelligence: Collaborative Knowledge Discovery (CKD). This technique combines the uncertain knowledge of several experts and the combined human knowledge is again fused with automatically extracted, well interpretable knowledge of a conventional KD system. Thus, a CKD system not only acquires more comprehensive knowledge, but also experience (knowledge about knowledge), meaning that it is able to explain automatically extracted rules to the human experts and to assess the interestingness (e.g., novelty or utility) of these rules. A CKD system will comprise self-awareness mechanisms (it must know what it knows) as well as environment-awareness mechanisms (it must know what human experts know or what they want to now). In order to reduce the effort for knowledge acquisition, a CKD system must learn proactively. There are many application areas for such CKD systems, e.g., in the field of technical data mining (quality control, process monitoring, etc.).