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Articles 1 - 4 of 4
Full-Text Articles in Business
Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr
Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr
Journal of International Technology and Information Management
The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain …
An Adaptive Neuro-Fuzzy System With Semi-Supervised Learning As An Approach To Improving Data Classification: An Illustration Of Bad Debt Recovery In Healthcare, Donghui Shi, Jozef Zurada, Jian Guan, Sandeep Goyal
An Adaptive Neuro-Fuzzy System With Semi-Supervised Learning As An Approach To Improving Data Classification: An Illustration Of Bad Debt Recovery In Healthcare, Donghui Shi, Jozef Zurada, Jian Guan, Sandeep Goyal
Journal of International Technology and Information Management
Business analytics has become an increasingly important priority for organizations today as they strive to achieve greater competitiveness. As organizations adopt business practices that rely on complex, large-scale data, new challenges also emerge. A common situation in business analytics is concerned with appropriate and adequate methods for dealing with unlabeled data in classification. This study examines the effectiveness of a semi-supervised learning approach to classify unlabeled data to improve classification accuracy rates. The context for our study is healthcare. The healthcare costs in the U.S. have risen at an alarming rate over the last two decades. One of the causes …
The Classification Performance Of Multiple Methods And Datasets: Cases From The Loan Credit Scoring Domain, Jozef Zurada, Niki Kunene, Jian Guan
The Classification Performance Of Multiple Methods And Datasets: Cases From The Loan Credit Scoring Domain, Jozef Zurada, Niki Kunene, Jian Guan
Journal of International Technology and Information Management
Decisions to extend credit to potential customers are complex, risky and even potentially catastrophic for the credit granting institution and the broader economy as underscored by credit failures in the late 2000s. Thus, the ability to accurately assess the likelihood of default is an important issue. In this paper the authors contrast the classification accuracy of multiple computational intelligence methods using five datasets obtained from five different decision contexts in the real world. The methods considered are: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT). …
Supporting The Virtual Community: Social Bookmarking As A User- Based Classification Scheme In A Knowledge Library, Nicole Lytle, Tony Coulsom
Supporting The Virtual Community: Social Bookmarking As A User- Based Classification Scheme In A Knowledge Library, Nicole Lytle, Tony Coulsom
Journal of International Technology and Information Management
Knowledge libraries hold the promise of widespread access to information available anywhere, anytime, freeing patrons from the geographical and temporal boundaries that currently exist. The classification of materials and subsequent searching of knowledge library content is an overall problem with many complex parts. Relevant classification is important for optimal information retrieval. This is especially important for the virtual communities that exist with extended organizations. Rooted in the virtual community and digital library literature, this paper develops a theory for improving the information classification and retrieval process of knowledge libraries that support virtual communities by applying social bookmarking techniques.