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Journal of International Technology and Information Management

Document Type

Article

Abstract

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 same data and improve their performance. In this paper we use concepts from both of these fields to create a modified and improved version of the standard stacking ensemble learning technique by using a genetic algorithm (GA) for creating the meta-classifier. We use concepts from distributed data mining to study different ways of distributing the data and use the concept of stacking ensemble learning to use different learning algorithms on each sub-set and create a meta-classifier using a genetic algorithm. We test the GA-based stacking algorithm on ten data sets from the UCI Data Repository and show the improvement in performance over the individual learning algorithms as well as over the standard stacking algorithm.

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