Performance Assessment of Learning Algorithms on Multi-Domain Data Sets

Abstract

This article describes how for the last few decades, data mining research has had significant progress in a wide spectrum of applications. Research in prediction of multi-domain data sets is a challenging task due to the imbalanced, voluminous, conflicting, and complex nature of data sets. A learning algorithm is the most important technique for solving these problems. The learning algorithms are widely used for classification purposes. But choosing the learners that perform best for data sets of particular domains is a challenging task in data mining. This article provides a comparative performance assessment of various state-of-the-art learning algorithms over multi-domain data sets to search the effective classifier(s) for a particular domain, e.g., artificial, natural, semi-natural, etc. In the present article, a total of 14 real world data sets are selected from University of California, Irvine (UCI) machine learning repository for conducting experiments using three competent individual learners and their hybrid combinations.