Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach

Zawbaa, Hossam M.; Emary, Eid; Groşan, Crina; Snåšel, Václav

doi:10.1016/j.swevo.2018.02.021

Cited by 116 publications

(44 citation statements)

References 23 publications

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“…The grey wolves being organized into four main levels [39] α wolves are the leaders and their responsibility is making decisions. β wolves are second-level wolves that help α wolves in taking decisions or the other activities.…”

Section: The Gwo Algorithm For the Mcvsk And Mvsk Portfolio Optimizatmentioning

confidence: 99%

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

et al. 2018

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Abstract:In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf's social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization.

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Section: The Gwo Algorithm For the Mcvsk And Mvsk Portfolio Optimizatmentioning

confidence: 99%

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

et al. 2018

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“…Recently, some hybrid bioinspired heuristic approaches were proposed to reduce the feature size of the input data such as the work of Zawbaa et al [23], whereas a hybrid algorithm is proposed to handle the large-dimensionality small-instance set feature selection problems. In [24], another algorithm is proposed to handle the feature selection problem using Levy Antlion optimization.…”

Section: Related Workmentioning

confidence: 99%

Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data

Mohammed

Alhayali

Bayat

et al. 2018

Scientific Programming

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A large amount of data being generated from different sources and the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data arises from many factors such as the high number of features, existence of lost data, and variety of data. One of the most effective solutions that used to overcome the huge amount of big data is the feature reduction process. In this paper, a set of hybrid and efficient algorithms are proposed to classify the datasets that have large feature size by merging the genetic algorithms with the artificial neural networks. The genetic algorithms are used as a prestep to significantly reduce the feature size of the analyzed data before handling that data using machine learning techniques. Reducing the number of features simplifies the task of classifying the analyzed data and enhances the performance of the machine learning algorithms that are used to extract valuable information from big data. The proposed algorithms use a new gene-weight mechanism that can significantly enhance the performance and decrease the required search time. The proposed algorithms are applied on different datasets to pick the most relative and important features before applying the artificial neural networks algorithm, and the results show that our proposed algorithms can effectively enhance the classifying performance over the tested datasets.

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“…A specific class of wrapper methods is represented by optimization approaches, inspired by natural selection, such as population-based or Genetic Algorithms (GAs) [10]. GAs are adaptive heuristic search algorithms that aim to find the optimal solution for solving complex problems.…”

Section: Introductionmentioning

confidence: 99%

GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets

et al. 2020

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Background: Feature selection is a crucial step in machine learning analysis. Currently, many feature selection approaches do not ensure satisfying results, in terms of accuracy and computational time, when the amount of data is huge, such as in 'Omics' datasets. Results: Here, we propose an innovative implementation of a genetic algorithm, called GARS, for fast and accurate identification of informative features in multi-class and high-dimensional datasets. In all simulations, GARS outperformed two standard filter-based and two 'wrapper' and one embedded' selection methods, showing high classification accuracies in a reasonable computational time.Conclusions: GARS proved to be a suitable tool for performing feature selection on high-dimensional data. Therefore, GARS could be adopted when standard feature selection approaches do not provide satisfactory results or when there is a huge amount of data to be analyzed.

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Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach

Cited by 116 publications

References 23 publications

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data

GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets

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