Intelligent Data Analysis: An International Journal

It is argued that in applications of concept learning from examples where not every possible category of the domain is present in the training set (i.e., many real world applications), classification performance can be improved by integrating suitable discriminative and characteristic models of classification. The suggested approach is to first discriminate between the categories present in the training set and then characterize each of these categories against all possible categories. To show the viability of this approach, a number of different discriminators and characterizers are integrated and tested. In particular, a novel characterization method that makes use of the information about the statistical distribution of feature values that can be extracted from the training examples is used. By using this method it is possible to control the degree of generalization and to deal with dependencies among features.

The self-organizing map (SOM) is an efficient tool for visualization of multidimensional numerical data. In this paper, an overview and categorization of both old and new methods for the visualization of SOM is presented. The purpose is to give an idea of what kind of information can be acquired from different presentations and how the SOM can best be utilized in exploratory data visualization. Most of the presented methods can also be applied in the more general case of first making a vector quantization (e.g. k-means) and then a vector projection (e.g. Sammon's mapping).

A concept called the decomposition of multivariable control rules is presented. Fuzzy control is the application of the compositional rule of inference and it is shown how the inference of the rule base with complex rules can be reduced to the inference of a number of rule bases with simple rules. A fuzzy logic based controller is applied to a simple magnetic suspension system. The controller has proportional, integral and derivative separate parts which are tuned independently. This means that all parts have their own rule bases. By testing it was found that the fuzzy PID controller gives better performance over a typical operational range then a traditional linear PID controller. The magnetic suspension system and the contact-less optical position measurement system have been developed and applied for the comparative analysis of the real-time conventional PID control and the fuzzy control.

Most fuzzy controllers must predefine membership functions and fuzzy inference rules to map numeric data into fuzzy linguistic values and make fuzzy reasoning work. In T.P. Hong, C.Y. Lee, Fuzzy Sets and Systems 84 (1996) 33–47, we proposed a general learning method for automatically deriving fuzzy-if-then rules and membership functions from a set of given training examples by merging the decision tables and membership functions. The merging order of the attributes, however, has great consequences on the accuracy of the final learning results. In this paper, we present appropriate heuristics to determine the merging order. Less relevant attributes will be processed earlier to reduce the complexity of the decision table. Experiments were also made, showing that our proposed heuristics demonstrate good performance.