An improved hierarchical partitioning fuzzy approach to pattern classification

Pattern classification has become an essential element in variety kind of realms such as engineering control and medical diagnosis applications. There are numerous approaches for classification and each of them proved effective in certain cases. Recently, a more general, accurate and efficient method is still desirable and the approaches of fuzzy logic have been successfully applied in this area. In this dissertation, a pattern classification system is realised based on an improved hierarchical partitioning fuzzy approach, which was initially proposed by I. Gadaras and L. Mikhailov [9]. The approach is able to directly extract rules from numerical data for classification and focus on achieving high accuracy with low expensiveness. A meaningful input partitioning technique for overlapping area and some adjustments on membership functions are highlighted. A pattern classification system based on proposed fuzzy methodology is programmed out by using Java language and JDBC database. This system is evaluated by Fisher Iris dataset and Wisconsin Breast Cancer dataset, which are famously employed for testing classification performance. Comparative results are analysed in detail with critical conclusion and future suggestions.