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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 …