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Outline

Title
Abstract
Introduction
Objective Functions
Classification Objectives
Extended Heuristic Method 1
Summary
Summary of the Thesis
Future Work
References

From approximative to descriptive fuzzy models

Profile image of Javier G . Marin-BlazquezJavier G . Marin-Blazquez

2003

Abstract

AI knowledge based systems have proven a very valuable tool to understand and model complex real-world problems. Unfortunately, human experts are often required to provide the knowledge or rules. Due to limited availability and sometimes experts' reluctance, or even inability, to give such expertise, knowledge acquisition has become the bottleneck in the development of knowledge based systems. However, there may well be a sensible amount of data that represents typical description of the domain problems. Therefore, data-driven techniques for automated generation of rules have been developed over the years to assist knowledge acquisition. While there have been efforts to make the automatically generated models transparent and readable, in most cases complexity ends up taking over. Fuzzy sets offers a way to deal with vague, imprecise or inaccurate information while reducing the complexity of knowledge representation. However, most of fuzzy rule generation methods, while fast, accurate and easily scalable to high dimensional problems, follow the so-called approximative approach, which works by creating and tuning the fuzzy rule bases and the fuzzy sets to best fit the data. Opposing this stands the descriptive approach, where semantics are as important as accuracy and the definition of the fuzzy sets is human given, thereby representing human interpretable concepts. Unfortunately, the methods to obtain descriptive models are generally rather slow, inaccurate and poorly scalable. It would be desirable to create a method to use the benefits of fast and efficient generation of the approximative approaches with the clarity and comprehensibility of descriptive approaches. The purpose of this thesis is the development of such a method. The thesis presents an effective and efficient approach for translating fuzzy rules that use approximative sets (accurate but unreadable) to rules that use descriptive sets and linguistic hedges of predefined meaning. It works by first generating rules that use approximative sets from training data, using a fast and accurate approximative algorithm that already exists. Then the resulting approximative rules are translated into descriptive ones. First, a heuristic conversion is performed to obtain a crude descriptive translation. Such a heuristically generated descriptive fuzzy model is then used to initialize a multi-objective GA. The GA, guided by the novel functional equivalence objectives, will fine-tune the heuristic translation into the final descriptive fuzzy rule set. Hedges that are useful for supporting such translations are provided. This thesis presents an improved version of more effective hedges specifically devised for trapezoidal fuzzy sets, to be applied to dilate or concentrate a given set by expanding or shrinking its constituent parts. It also introduces three new hedges not existing in the literature. Considerable experimental studies have been carried out, on the issues of the accuracy and transparency of the descriptive rules generated by the proposed approach. These include comparative analysis between alternative modelling approaches, v vi Declaration I hereby declare that I composed this thesis entirely myself and that it describes my own research.

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