Anil Kashyap

A New Dynamic Rule Activation Method for Extended Belief Rule-Based Systems

Data incompleteness and inconsistency are common issues in data-driven decision models. To some extend, they can be considered as two opposite circumstances, since the former occurs due to lack of information and the latter can be regarded as an excess of heterogeneous information. Although these issues often contribute to a decrease in the accuracy of the model, most modeling approaches lack of mechanisms to address them. This research focuses on an advanced belief rule-based decision model and proposes a dynamic rule activation (DRA) method to address both issues simultaneously. DRA is based on “smart” rule activation, where the actived rules are selected in a dynamic way to search for a balance between the incompleteness and inconsistency in the rule-base generated from sample data to achive a better performance. A series of case studies demonstrate how the use of DRA improves the accuracy of this advanced rule-based decision model, without compromising its efficiency, especially when dealing with multi-class classification datasets. DRA has been proved to be beneficial to select the most suitable rules or data instances instead of aggregating an entire rule-base. Beside the work performed in rule-based systems, DRA alone can be regarded as a generic dynamic similarity measurement that can be applied in different domains.

An intelligent decision support tool based on belief rule-based inference methodology

Taking into account the need of handling hybrid information with uncertainty in human decision making, a new belief rule-base inference methodology (RIMER) has been recently proposed. RIMER approach and its relevant extensions have proved to be highly positive solving decision problems. However, for an end user it is difficult to implement the methods and algorithms from the raw equations in order to solve a specific problem. This paper presents a decision support tool based on the RIMER approach that facilitates its implementation and use to end-users. The overall structure and main functionalities of the tool are outlined, followed by an example to illustrate the use of this tool for applications.

Dynamic rule activation for Extended Belief Rule Bases

Incompleteness and inconsistent situations are common in most rule-based decision support systems (DSS). However, most rule inference methods do not provide procedures to specifically tackle and/or analyze them. This research presents a single approach for both incompleteness and inconsistency issues with a simple yet effective method. During the rule activation step, data incompleteness and inconsistency may be seen as paired situations, since the former appears due to lack of information while the latter can be represented as an excess of heterogeneous information activated. To effectively take advantage of this fact, this research presents a Dynamic Rule Activation (DRA) method, which searches for a balance between both incomplete and inconsistent situations to improve the overall performance of the DSS. Although DRA is designed as a flexible method, able to work with most similarity measures, in this research it is applied in the context of Extended Belief Rule-Bases (E-BRBs). The case studies illustrated in this research demonstrate how the use of DRA can improve the accuracy of E-BRB based decision support models. In this regard, the RIMER+ model and the simple weighted average of the activated rules were tested with and without using DRA as pre-processing method.

A GIS-based Spatial Decision Support Tool Based on Extended Belief Rule-Based Inference Methodology

Nowadays, most Spatial Decision Support Systems (SDSSs) are designed to solve a specific problem in a given region. This fact makes rather difficult or even impossible to develop comparative analyses and studies among different solutions. This research presents a generic rule-based spatial decision support software tool, able to approach most spatial decision problems within a single framework. To achieve this, the rule-based RIMER+ decision model was embedded in a Geographic Information System (GIS) environment. Such system was named Spatial RIMER+, and is able to consider expert knowledge, data uncertainty and both spatial and nonspatial information during the decision making process.

A Novel Spatial Belief Rule-Based Intelligent Decision Support System

Real-world decision problems are usually associated with a certain geographical area, and therefore can and should be geographically referenced in most of cases. While traditional Decision Support Systems (DSSs) ignore the spatial dimension of the problem, most Geographic Information System (GIS)-based Spatial Decision Support Systems (SDSSs) focus mainly on the spatial analysis of the problem, avoiding other relevant factors like uncertainty and incompleteness of data sets. This research is based on a recently developed intelligent belief rule-based DSS, called RIMER+, which is shown to be capable of capturing vagueness, incompleteness, uncertainty, and nonlinear causal relationships in an integrated way. The main contribution of this research is to explore the possibilities of achieving a higher degree of integration of DSSs in a GIS environment, i.e., integration of RIMER+ within GIS system by using an embedded approach, which not only enhances further the capability and applicability of the RIMER+ by integrating the spatial component of the problem into the decision making process, but also takes advantage of the GIS software capabilities in terms of spatial analysis and visualization. Finally, this research employs a comparative case study to demonstrate performance of the proposed Spatial RIMER+ methodology against the well-known Geographically Weighted Regression (GWR) methodology.Real-world decision problems are usually associated with a certain geographical area, and therefore can and should be geographically referenced in most of cases. While traditional Decision Support Systems (DSSs) ignore the spatial dimension of the problem, most Geographic Information System (GIS)-based Spatial Decision Support Systems (SDSSs) focus mainly on the spatial analysis of the problem, avoiding other relevant factors like uncertainty and incompleteness of data sets. This research is based on a recently developed intelligent belief rule-based DSS, called RIMER+, which is shown to be capable of capturing vagueness, incompleteness, uncertainty, and nonlinear causal relationships in an integrated way. The main contribution of this research is to explore the possibilities of achieving a higher degree of integration of DSSs in a GIS environment, i.e., integration of RIMER+ within GIS system by using an embedded approach, which not only enhances further the capability and applicability of the RIMER+ by integrating the spatial component of the problem into the decision making process, but also takes advantage of the GIS software capabilities in terms of spatial analysis and visualization. Finally, this research employs a comparative case study to demonstrate performance of the proposed Spatial RIMER+ methodology against the well-known Geographically Weighted Regression (GWR) methodology.

Financial structure of PPPs deals post‐GFC: an international perspective

Purpose – The purpose of this paper is to explore the implications of the Global Financial Crisis (GFC) on Public Private Partnership (PPP) markets around the world. Specifically, it aims to highlight the extent of over reliance on debt finance, as well as the conditions needed to attract enhanced levels of institutional investment into key infrastructural provision.Design/methodology/approach – Quantitative insight for the paper is derived from the Infrastructure Online Database. The Infrastructure Journal (IJ) Online Database profiles PFI/PPP deals around the world depicting the key actors involved, as well as the capital value of deals and the financial structures applied in terms of debt, equity and Multilateral and Government Finance. The quantitative insight derived from the IJ database is complemented by interview evidence and forum‐based discussion. In total, 38 interviews were conducted with a diverse range of key stakeholder groupings from across the public and private sectors, including governm...