Improving Efficiency of Relational Classification Technique Based on Relational Database Using Contribution of Tables

Classification is one of the most popular data mining tasks with a wide range of applications. Many algorithms have been proposed to build an accurate classifier. These algorithms work for a single Table as an input but in real-world applications most of the data relies on multiple Tables. As converting data from multiple relations into single flat relation usually causes many problems, development of classification across multiple database relations becomes important. There have been many approaches for classification, such as neural networks and support vector machines. However, they can only be applied to data in single flat relations. It is counterproductive to convert multi-relational data into single flat Tables because such conversion may lead to the generation of huge relation and loss of essential semantic information. In this work, we propose algorithm for multi-relational classification (MRC) which uses weighted voting technique to combine classifiers to get class label based on the contribution of Tables. This will classify the instance accurately and efficiently.