Zahra Jiryaei Sharahi; Yahia Zare Mehrjerdi; Mohammad Saleh Owlia; Masoud Abessi
Abstract
In a data-driven decision-making process, there are various types of data that should be thoroughly processed and analyzed. Data mining is a well-recognized method to obtain such information by analyzing data and transforming it into actionable insights for further use. Among the various data mining ...
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In a data-driven decision-making process, there are various types of data that should be thoroughly processed and analyzed. Data mining is a well-recognized method to obtain such information by analyzing data and transforming it into actionable insights for further use. Among the various data mining techniques such as classification, clustering, and association rules, this research focused on classification techniques and presented an innovative regression-based learning approach in the decision tree (DT) models. DT algorithms are easy-to-understood and can work with different data types including continuous, discrete, and non-numerical. Despite a large number of existing studies, which attempt to enhance the performance of the DT models, there is still a gap in accurately extracting knowledge from databases. In this research, this issue is addressed by exploiting regression and coefficient of determination (R2) methods in a DT. The proposed tree provides new insights in the following aspects: split criterion, handling continuous and discrete variables, labeling leaf node, pruning process by stopping criteria and tree evaluation. The superiority of the proposed algorithm is demonstrated using a real-world hospital database and a comparison with existing approaches is provided. The results showed that the proposed algorithm outperforms the existing methods in terms of higher accuracy and lower complexity.
Mohammad Saleh Owlia; Kosar Roshani; Mohammad Hossein Abooei
Abstract
In the age of a knowledge-based economy, identifying, measuring, and managing the intellectual capital (IC) of organizations has become very significant. These depend on identifying the main components of intellectual capital and their relationships. So far, however, no study has been conducted to clarify ...
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In the age of a knowledge-based economy, identifying, measuring, and managing the intellectual capital (IC) of organizations has become very significant. These depend on identifying the main components of intellectual capital and their relationships. So far, however, no study has been conducted to clarify the interactions among those components or to develop a model for laying out a hierarchy of IC components. There is, indeed, an urgent need to analyze the behavior of IC components so that the corresponding policies may be successfully implemented. This paper aims to prioritize the IC components based on the identified relationships among the IC components with a focus on the banking industry. A literature review was used to identify the 16 most important IC components. At the first stage, the Interpretive Structural Modeling technique was practiced to determine the interrelationships among these components, based on the data gathered from the Export Development Bank of Iran. The interconnections between the components were clarified. At the second stage, the application of Analytic Network Process for the prioritizing of IC components has been demonstrated. MICMAC analysis and classifying them into four categories including the autonomous, driver, dependent, and linkage components regarding their driving and dependence power is a new effort in the field of IC. A hierarchical structure was proposed through leveling of the components. And finally, the importance and priorities of the components are calculated with the help of the fuzzy analytic network process. The adoption of such an ISM-ANP model of IC components in the banking industry would provide insights for managers, decision-makers and policymakers for a better understanding of these components and to focus on the major components while managing their IC in their organizations.