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Great deluge (GD) algorithm same as other metaheuristics can solve feature selection problem. The GD imitates that in a great deluge someone climbing a hill and attempt to progress in any direction that does not get his/her feet wet in the expectation of discovering a way up when the water Level rises. The drawbacks of GD are: 1) a local search, which may lead the algorithm toward a local optima and 2) a challenging estimation of quality of the final solution in solving most of the problems. In this paper, for the first issue, a population-based great deluge (popGD) algorithm with additional recurrence operation is proposed. This operation is an imitation of no progress of hill climber after a long time; the climber tries to move small steps even downward in hope of finding better way to climb. For the second problem, a technique with an automate alteration of the Level is proposed. The statistical analysis of the results from 25 test functions and 18 benchmark feature selection problems supports the ability of the method. Finally a real-world academician data are employed to perform feature selection and execute classification result with selected features. [ABSTRACT FROM AUTHOR]

An innovative algorithm is developed for obtaining spreadsheet regression measures used in computing out-of-sample statistics. This algorithm alleviates the leave-one-out computational simulation complexity and memory size problems perceived in computing these statistics. Hence, the purpose of this article is to describe a computationally enhanced algorithm that gives spreadsheet users advanced regression capabilities thereby adding a new dimension to spreadsheet regression operations. These statistics include diagonals of the hat matrix, legitimate forecasting intervals, and PRESS residuals. These computational innovations promote learning while eliminating spreadsheet inadequacies thereby making spreadsheet regression attractive to academicians in teaching and practitioners in acquiring further application competence. [ABSTRACT FROM AUTHOR]