Based on a panoramic analysis of the serious Yangtze deluge in 1998 and its hydrological traits, the authors elucidate the formative factors of the deluge and review existing flood-control policies in retrospect. In a...Based on a panoramic analysis of the serious Yangtze deluge in 1998 and its hydrological traits, the authors elucidate the formative factors of the deluge and review existing flood-control policies in retrospect. In addition, some countermeas-ures are suggested for coping with similar calamities in the near future.展开更多
Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among t...Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.展开更多
文摘Based on a panoramic analysis of the serious Yangtze deluge in 1998 and its hydrological traits, the authors elucidate the formative factors of the deluge and review existing flood-control policies in retrospect. In addition, some countermeas-ures are suggested for coping with similar calamities in the near future.
基金This research is part of a project funded by Imam Abdulrahman Bin Faisal University,under Grant Number 2020-083-BASRC.
文摘Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.
