Title: Multiple-case outlier detection in least-squares regression model using Quantum-inspired Evolutionary Algorithm
Authors: Mozammel H.A. Khan
Addresses: Department of Computer Science and Engineering, East West University, 43 Mohakhali CA, Dhaka 1212, Bangladesh
Abstract: In ordinary statistical methods, multiple outliers in least-squares regression model are detected sequentially one after another, where smearing and masking effects give misleading results. If the potential multiple outliers can be detected simultaneously, smearing and masking effects can be avoided. Such multiple-case outlier detection is of combinatorial nature and 2N – 1 sets of possible outliers need to be tested, where N is the number of data points. This exhaustive search is practically impossible. Like other combinatorial applications, evolutionary algorithms may play a vital role in multiple-case outlier detection problem. In this paper, we have used Quantum-inspired Evolutionary Algorithm (QEA) for multiple-case outlier detection in least-squares regression model. An information-criterion-based fitness function incorporating extra penalty for number of potential outliers has been used for identifying the most appropriate set of potential outliers. Experimental results with four data sets from statistical literature show that the QEA effectively detects the most appropriate set of outliers.
Keywords: combinatorial problems; least squares regression; multiple-case outlier detection; QEA; quantum-inspired evolutionary algorithms; multiple outliers.
International Journal of Artificial Intelligence and Soft Computing, 2010 Vol.2 No.3, pp.174 - 185
Published online: 17 Feb 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article