Robust regression and outlier detection
Robust regression and outlier detection
Numerical Methods for Engineers: With Software and Programming Applications
Numerical Methods for Engineers: With Software and Programming Applications
Applying Genetic Algorithms to Outlier Detection
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic algorithms for outlier detection and variable selection in linear regression models
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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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.