Hybrid Approaches for Classification Under Information Acquisition Cost Constraint

The practical use of classification systems may be limited because the current classification systems do not allow decision makers to incorporate cost constraint. For example, in several financial applications (loan approval, credit scoring, etc.) an applicant is asked to submit a processing fee with the application (Mookerjee and Mannino 1997). The processing fee may be used to validate the information entered in the application. From an economic standpoint, it is important that the cost of validating the information not exceed the processing fee. Traditional classification systems do not allow the decision maker to incorporate information acquisition cost constraint. We term the problem of designing a classification system, where information acquisition costs are considered,astheproblemofclassificationwithinformationacquisitioncostconstraint(CIACC). TheCIACCproblemisaNP hard problem and is very difficult to solve to optimality

A Bayesian-Decision Analysis Framework for the Financial Evaluation of Automatic Incident Detection Devices

We propose a Bayesian decision analysis framework for the evaluation of automatic incident detection (AID) tools in intelligent transportation systems. The proposed framework can be used by decision makers for financial analysis of AID devices, identify appropriate AID device locations and develop an AID device replacement schedule

Reengineering the Human Resource Information System at Gamma

In 1997, Gamma Health Care Systems embarked on a redesign project for their Human Resource Information System (HRIS). Redesign involved major changes to the existing system to guarantee a very high level of service. This case describes the efforts of the Human Resource Department (HRD) to redesign its HRIS to better meet enterprise-wide goals of cost effectiveness and efficiency. The reengineering project transformed the HRD from a historic role of transaction processing to one of a strategic partner

Reengineering Comes To the Nonprofit Sector: A Case Study of Goodwill Industries of the Laurel Highlands Incorporated

Traditionally, not-for-profit organizations did not worry about management. “Twenty years ago, management was a dirty word for those involved in nonprofit organizations ... now most of them have learned that nonprofits need management even more than business does, precisely because they lack the discipline of the bottom line” (Drucker, 1989). Not-for-profit organizations are in the initial stages of using strategic management (Wortman, 1988; Bryson, 1988; Karagozoglu and Seglund, 1989; Harvey and McCrohan, 1988). Therefore, it is not surprising that reengineering and business process redesign have been given little attention by not-for-profits

The Wisconsin division of narcotics enforcement uses multi-agent information systems to investigate drug crimes

We built a multi-agent information system (MAIS) called Sherpa using distributed artificial intelligence architecture. The system integrates distributed knowledge sources and information to help the Wisconsin Division of Narcotics Enforcement (WDNE) make decisions about the level of charges against a drug crime suspect. Sherpa outperforms the existing system in the identification of criminals. B efore drug investigators can arrest suspected criminals, they must identify suspects, collect data about them, and analyze the data. They gather information from different sources external to the drug enforcement agency, such as the Internal Revenue Service and the Division of Criminal Investigation, combining the data with that from internal sources, removing redundancies, and identifying patterns in the data. Drug enforcement agencies face conditions that hinder the use of information systems and the diffusion of information. The issues they need to address in using information systems arise from both external and internal factors. The external factors include a dependency on other agencies for funding projects, the beliefs of politicians, and the assumption that they can work during regular business hours against an enemy that works round the clock. The internal factors include agents poorly trained in use of information technology We designed and implemented a distributed problem-solving multi-agent in

A Case Study of the Military Utility of Telemedicine

This paper is designed to relate the rationale used by the Department of Defense to determine the military utility of the Joint Medical Operations – Telemedicine Advanced Concept Technology Demonstration (JMO-T ACTD). The paper also develops Critical Operational Issues (COI) and Measures of Effectiveness (MOE) as methodologies for investigating the military utility of telemedicine. In order to meet increasing global crises, the U.S. military must find ways to more effectively manage manpower and time. Joint Medical Operations – Telemedicine (JMO-T) has been developed by the Department of Defense (DOD) to collect and transmit near-real-time, far-forward medical data and to assess how this improved capability enhances medical management of the battlespace. JMO-T has been successful in resolving uncertain organizational and technological military deficiencies and in improving medical communications and information management. The deployable, mobile Telemedicine Teams are the centerpieces of JMO-T. These teams have the capability of inserting essential networking and communications capabilities into austere theaters and establishing an immediate means for enhancing health protection, collaborative planning, situational awareness, and strategic decision-making

A potential use of data envelopment analysis for the inverse classification problem

We propose a methodology that uses data envelopment analysis (DEA) for solving the inverse classification problem. An inverse classification problem involves finding out how predictor attributes of a case can be changed so that the case can be classified into a different and more desirable class. For a binary classification problem and non-negative decision-making attributes, we show that under the assumption of conditional monotonicity, and convexity of classes, DEA can be used for inverse classification problem. We illustrate the application of our proposed methodology on a hypothetical and a real-life bankruptcy prediction data.Classification Data envelopment analysis Linear programming Discriminant analysis