Expanding the Training Data Space Using Emerging Patterns and Genetic Methods
Hamad Alhammady, Kotagiri Ramamohanarao · 2005
Classification is a major problem in machine learning. Many classifiers have been developed recently. However, the performance of these classifiers is proportional to the knowledge obtained from the training data. As a result, traditional classifiers can not perform very well when the training data space is very limited. In this paper, we propose a new approach to expand the training data space (ETDS) using emerging patterns (EPs) [4] and genetic methods (GMs) [7]. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. GMs are evolutionary methods that incorporate computational techniques inspired by biology [8]. We combine the power of EPs and GMs to expand the training data space before applying standard classifiers. The expansion process is performed by generating more training instances using four techniques. An extensive experimental evaluation carried out on a number of datasets shows that our approach has a great impact on the performance of many traditional classifiers.