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Full-Text Articles in Business
Entropy Based Feature Selection For Multi-Relational Naïve Bayesian Classifier, Vimalkumar B. Vaghela, Kalpesh H. Vandra, Nilesh K. Modi
Entropy Based Feature Selection For Multi-Relational Naïve Bayesian Classifier, Vimalkumar B. Vaghela, Kalpesh H. Vandra, Nilesh K. Modi
Journal of International Technology and Information Management
Current industries data’s are stored in relation structures. In usual approach to mine these data, we often use to join several relations to form a single relation using foreign key links, which is known as flatten. Flatten may cause troubles such as time consuming, data redundancy and statistical skew on data. Hence, the critical issues arise that how to mine data directly on numerous relations. The solution of the given issue is the approach called multi-relational data mining (MRDM). Other issues are irrelevant or redundant attributes in a relation may not make contribution to classification accuracy. Thus, feature selection is …
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). …
An Excel Planning And Teaching Decision Aid For Bonner's Conceptual Framework, Freddie Choo, Kim Tan
An Excel Planning And Teaching Decision Aid For Bonner's Conceptual Framework, Freddie Choo, Kim Tan
Journal of International Technology and Information Management
Bonner (1999, 2008) prescribes a conceptual framework entitled “Choosing teaching methods based on learning objectives: An integrative framework” to plan and teach accounting. Bonner’s conceptual framework involves a holistic-mapping-process in which an instructor holistically maps a set of accounting learning objectives (ALO), general learning objectives (GLO), necessary conditions (NC), and teaching methods (TM). The scope of this paper is aimed at developing an Excel planning and teaching decision aid (EDA) for Bonner’s holistic-mapping- process. This EDA neither replaces nor supplants the conventional judgment-based planning and teaching process. By presenting and making the EDA available, perhaps accounting information systems researchers will …
Use Of A Fast Information Extraction Method As A Decision Support Tool, Mahmudul Sheikh, Sumali Conlon
Use Of A Fast Information Extraction Method As A Decision Support Tool, Mahmudul Sheikh, Sumali Conlon
Journal of International Technology and Information Management
Ad-hoc extraction of information from documents can ensure the transparency of decisions made by an organization. Different Information Extraction methods have been applied to extract information from various domains. Most widely known methods use manually annotated training documents that require high development time. The automated training methods are not scalable to large application domains. We have developed a semi-automated knowledge-engineering method for building the knowledge-base with minimal efforts. Because our method reduces manual processing of the training data, the development process is very fast. We have developed a prototype application to extract information from the project-reports of the American Recovery …