Rules Extraction Based on Data Summarisation Approach Using DARA
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Evolutionary multi-feature construction for data reduction: A case study
Applied Soft Computing
Feature set reduction by evolutionary selection and construction
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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This paper addresses the question whether or not the descriptive accuracy of the DARA (Dynamic Aggregation of Relational Attributes) algorithm benefits from the feature construction process. This involves solving the problem of constructing a set of relevant features used to generate patterns representing records in the TF-IDF weighted frequency matrix. In this paper, feature construction will be applied to enhance the results of the data summarisation approach in learning data stored in multiple tables with a high number of one-to-many relations. It is expected that the predictive accuracy of a classfication problem can be improved by improving the descriptive accuracy of the data summarisation approach, provided that the summarised data is fed into the target table as one of the features considered in the classification task.