See for example (Abdullah and Jaddi Citation2010 Aljarah et al. Searching for a minimal subset of original feature set with the same level of discernibility of all features using metaheuristic algorithms is a common method in literature. Rough set feature selection is performed by only using the data and no extra information. Rough set theory presented in (Pawlak Citation1982) as a filter-based tool determines the relations between conditional features and decision feature. Selection of a subset of the full feature set while still describes the original and full feature set with less information loss is defined as feature selection (Han, Kamber, and Pei Citation2011). Finally a real-world academician data are employed to perform feature selection and execute classification result with selected features.įeature selection is considered as an important and necessary step (pre-processing) before performing any data mining task and data engineering. The statistical analysis of the results from 25 test functions and 18 benchmark feature selection problems supports the ability of the method. For the second problem, a technique with an automate alteration of the Level 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. In this paper, for the first issue, a population-based great deluge (popGD) algorithm with additional recurrence operation is proposed. 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. 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. Great deluge (GD) algorithm same as other metaheuristics can solve feature selection problem.
0 kommentar(er)
