A combined data mining approach using rough set theory and case-based reasoning in medical datasets

Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of co-occurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge

Rough set approach to case-based reasoning application

[[abstract]]The case-based reasoning becomes a novel paradigm that solves a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation. In general, the traditional representation of cases is too simple and is not well structured to support the decision-making in organization. Furthermore, the similarity testing of case-based reasoning is very time-consuming. Therefore, a novel approach to represent the knowledge of cases in an explicit manner and to search similar cases in an efficient way is desired. An Extensible Markup Language-based representation formulated with the Zachman framework is proposed in this paper. Through a rough set based approach, case-based reasoning becomes more efficient and complexity of computation of the similarity testing is significantly reduced. (C) 2003 Elsevier Ltd. All rights reserved.[[note]]SC