Narrow Results

The mass assignment ID3 (MA-ID3) algorithm for generating linguistic decision trees is introduced together with the mass assignment semantics for linguistic variables. The potential of this algorithm for learning control rules is illustrated by means of the Van de Pol system. A data set of control paths is generated using an existing on-line controller. This is then used to generate a set of quantified linguistic control rules. The effectiveness and robustness of this rule-base is then demonstrated.

The problem of automating the sensing and classification of odours is one which promises a wide range of industrial applications. During the INTESA project, a prototype electronic nose was developed, using sensors based on novel conducting polymer materials and also more traditional MOS materials. The software component of the prototype processes the transient resistance change signals recorded by the hardware, and classifies the odour sample into one of a number of "odour classes". This paper describes two of the soft computing methods investigated for learning classification rules in this domain. The first method builds on previous work done on the Fril data browser, using clustering, fuzzy matching, Fril rules and evidential logic rules. The second method uses a fuzzy extension of the ID3 decision tree induction method, called "mass assignment tree induction (MATI)". Some of the results of applying these methods to data obtained from the INTESA prototype are presented and discussed.

We propose label semantics as an integrated representation framework for probabilistic uncertainty and fuzziness in multiple-attribute decision making problems. Linguistic attribute hierarchies are then introduced as a means of modelling the complex and often imprecise functional relationships between low-level attributes or measurements and high-level decision or classification variables. Within this framework we introduce linguistic decision trees as a tool for information aggregation in multi-attribute decision problems and describe the process of information propagation through a hierarchy of linked decision trees. In addition, we consider the ranking of different alternatives or examples based on their linguistic descriptions of a high-level utility variable. Finally, we discuss how linguistic decision trees embedded in attribute hierarchies can be learnt from data.

We present two methods of modelling ordered datasets using Baldwin's mass assignment. The first method generates a simplified memory-based fuzzy belief updating model. Results are given in application to particle classification and facial feature detection. The second method uses a new, high level, fuzzy trend feature based on a set of fuzzy trend prototypes. These prototypes are closely related to human perceptions of shape in ordered series. The models generated using this method are concise and linguistically clear glass box models. Results are given in application to sunspot and simple sinewave data series.

This paper explores the relationship between object level intuitionistic fuzzy sets and predicate based intuitionistic fuzzy sets. Mass assignment uses a process called semantic unification to evaluate the degree to which one set supports another. Intuitionistic fuzzy sets are mapped onto a mass assignment framework and the mass assignment semantic unification operator is generalised to support both mass assignment and intuitionistic fuzzy sets. Transfer of inconsistent and contradictory evidence is also dealt with. As a consequence, by conjoining the mutual semantic unification of two sets a similarity measure emerges.