MD Method in Solving Multiple Criteria Decision Problems Arising from Investment Systems

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Interactive Management: An Emancipatory Methodology

An erosion of the power of people and their communities to control any more than a fragment of their future continues. Educational and governmental policy decisions are, for the most part, mandated from above and/or outside the community. This paper introduces a model which reflects Freire's humanizing pedagogy. This method is a vehicle, an avenue of empowerment for people and their communities to control their own destiny and the future of their community. The model is Interactive Management (IM) and its derivative, The CogniScope System. IM is a specialized system of management using collaborative teamwork to define and resolve highly complex issues. The process includes the integration of the diversity of perceptions of the participants with regard to the given ssues, builds consensus and joint ownership of the process and/or product, and creates a collaborative action plan to accomplish the participants' goals. The principal intangible outcome of the process, however, is the reflective participation, which leads to action, and the learning that occurs among the community of stakeholders present. The process of Interactive Management (IM), enhanced through the use of the CogniScope System, is described and examples of its application by First Nation peoples are shared.     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.     

Clustering patents using non-exhaustive overlaps

Patent documents are unique external sources of information that reveal the core technology underlying new inventions.Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments.This research incorporates an ontology schema to extract and represent patent concepts.A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery.The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims,a feature that enables the grouping of patents that define related key innovations.Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation.The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.