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California State University, San Bernardino
Journal of International Technology and Information Management
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Full-Text Articles in Business
A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms, Riyaz Sikora, O'La Hmoud Al-Laymoun
A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms, Riyaz Sikora, O'La Hmoud Al-Laymoun
Journal of International Technology and Information Management
With the massive increase in the data being collected as a result of ubiquitous information gathering devices, and the increased need for doing data mining and analyses, there is a need for scaling up and improving the performance of traditional data mining and learning algorithms. Two related fields of distributed data mining and ensemble learning aim to address this scaling issue. Distributed data mining looks at how data that is distributed can be effectively mined without having to collect the data at one central location. Ensemble learning techniques aim to create a meta-classifier by combining several classifiers created on the …
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 …
A Hybrid Machine Learning System For Stock Market Forecasting, Lokesh Kumar, Anvita Pandey, Saakshi Srivastava, Manuj Darbari
A Hybrid Machine Learning System For Stock Market Forecasting, Lokesh Kumar, Anvita Pandey, Saakshi Srivastava, Manuj Darbari
Journal of International Technology and Information Management
A hybrid machine learning system based on Genetic Algorithm (GA) and Time Series Analysis is proposed. In stock market, a technical trading rule is a popular tool for analysts and users to do their research and decide to buy or sell their shares. The key issue for the success of a trading rule is the selection of values for all parameters and their combinations. However, the range of parameters can vary in a large domain, so it is difficult for users to find the best parameter combination. In this paper, we present the Genetic Algorithm (GA) to overcome the problem …
Grey Situation Decision-Making Algorithm To Optimize Silicon Wafer Slicing, Che-Wei Chang, William Yu Chung Wang
Grey Situation Decision-Making Algorithm To Optimize Silicon Wafer Slicing, Che-Wei Chang, William Yu Chung Wang
Journal of International Technology and Information Management
The slicing of Silicon wafer is a complex manufacturing process in producing the raw materials for electronic chips and requires the efforts to effectively monitor the stability in production line and ensure the quality for the products composed of different shapes and materials. Human decision failure and other analytical errors are the most common source of management problems in such manufacturing stage. This paper presents a case regarding the silicon wafer manufacturing to examine the response to quality errors. The study has adopted the approach of grey situation decision-making algorithm for problem detection that suggests a technique to attain the …
A Genetic Algorithm Assisted Hybrid Approach To Web Information Integration, Jia-Lang Seng, Ming-Hsiung Ying
A Genetic Algorithm Assisted Hybrid Approach To Web Information Integration, Jia-Lang Seng, Ming-Hsiung Ying
Journal of International Technology and Information Management
No abstract provided.