A new feature selection algorithm based on binary ant colony optimization

Kashef, Shima; Nezamabadi‐pour, Hossein

doi:10.1109/ikt.2013.6620037

Cited by 43 publications

(25 citation statements)

References 9 publications

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“…The Advanced Binary Ant Colony Optimization (ABACO) algorithm [10] is a specification of the Ant Colony Optimization (ACO) algorithm [20] for the feature selection problem. The ACO inspiration came from the foraging behavior of real ant colonies.…”

Section: A Advanced Binary Ant Colony Optimizationmentioning

confidence: 99%

“…The path's length and the amount of pheromone previously laid by the colony are decisive criteria for an ant to choose a certain path to go to another node. As more ants cross a path, the trail of pheromone is reinforced [10] [21].…”

Section: A Advanced Binary Ant Colony Optimizationmentioning

confidence: 99%

“…In this paper we compare the performance of three binary versions of well known swarm intelligence algorithms applied to feature selection. The Binary Fish School Search (BFSS) algorithm is modeled according to the behavior of a fish school [9], the Advanced Binary Ant Colony Optimization (ABACO) algorithm is a metaphor of the foraging behavior of real ant colonies [10] and the social behavior of a birds flock inspired the development of the Binary Particle Swarm Optimization (BPSO) algorithm [11]. These meta-heuristics are able to find good sets of features without the need to exhaustively explore the entire search space.…”

Section: Introductionmentioning

confidence: 99%

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Metaheuristics for feature selection in handwritten digit recognition

Seijas

Carneiro

Santana

et al. 2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

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Recognition of handwritten digits by computers is a common research topic in the pattern recognition area and has application in several domains. Many techniques can be applied in order to maximize the recognition performance, such as image preprocessing, feature extraction, feature selection and classification stages. This paper focuses on the assessment of three swarm intelligence optimization algorithms for feature selection optimization, called Binary Fish School Search (BFSS), Advanced Binary Ant Colony Optimization (ABACO) and Binary Particle Swarm Optimization (BPSO), for the recognition of handwritten digits. These meta-heuristics were applied to the well-known handwritten digit database MNIST, preprocessed with the CDF 9/7 Wavelet Transform. We used support vector machine (SVM) for the classification task. A considerable reduction in the number of features used for digit classification on the MNIST database with a small loss in the classification rates was observed.Keywords-feature selection; binary optimization; support vector machine; wavelet transform; handwritten digit recognition; binary fish school search; advanced binary ant colony optimization; binary particle swarm optimization.

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Section: A Advanced Binary Ant Colony Optimizationmentioning

confidence: 99%

Section: A Advanced Binary Ant Colony Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metaheuristics for feature selection in handwritten digit recognition

Seijas

Carneiro

Santana

et al. 2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

View full text Add to dashboard Cite

show abstract

“…, (i=1,2,…,n) (5) where is the number of features, is the number of classes, is the number of samples of the -th feature in class , is the -th training sample of the -th feature in class , and are the averages of the th feature of all samples and of the samples of class , respectively.…”

Section: Entropy Entropymentioning

confidence: 99%

“…By using these techniques, we can achieve the reasonable solutions without having to explore the entire solution space. Some of meta-heuristic algorithms used in the field of feature selection are as the following but are not limited to them: genetic algorithm (GA) [4], ant colony optimization (ACO) [5], gravitational search algorithm (GSA) [6][7][8] and particle swarm optimization (PSO) [9].…”

Section: Introductionmentioning

confidence: 99%

A hybrid feature selection method for high-dimensional data

Taheri

Nezamabadi‐pour

2014

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)

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Feature selection is one of the important preprocessing steps in analyzing high dimensional datasets. In this paper, first the ensemble of three different filter ranking methods including: Information Gain (IG), ReliefF and Fscore are used to reduce the dimension of datasets. Afterward, reduced data are utilized as inputs of the meta-heuristic algorithm, Improved Binary Gravitational Search Algorithm (IBGSA), for selecting optimal subset of features with highest classification accuracy rate. In order to evaluate the proposed method, it is applied to several high-dimension standard datasets and the results in terms of classification accuracy and feature reduction rate are presented. The experimental results confirm the capability of the proposed algorithm.

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Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

Balakrishnan

Dhanalakshmi

2022

Concurrency and Computation

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Optimal feature selection of a high-dimensional micro-array datasets has gained a significant importance in medical applications for early detection and prevention of disease. Traditional Optimal feature selection percolates through a population-based meta-heuristic optimization technique, a Machine Learning classifier and traditional wrapper method for transforming the original feature set into a better feature set.These techniques require a number of iterations for the convergence of random solutions to the global optimum with high-dimensionality issues such as over-fitting, memory constraints, computational costs, and low accuracy. In this article, an efficient equilibrium optimization technique is proposed for an optimized feature selection that increases the diversity of the population in the search space through Random Opposition based learning and classify the best features using a 10-fold cross-validation-based wrapper method. The proposed method is tested with six standard micro-array datasets and compared with the conventional algorithms such as Marine Predators Algorithm, Harris Hawks Optimization, Whale Optimization Algorithm, and conventional Equilibrium Optimization. From the statistical results using the standard metrics, it is interpreted that the proposed method converges to the global minimum in a few iterations through optimized feature selection, fitness value and higher classification accuracy. This proves its efficacy in exploring and finding a better solution as compared to the counterpart algorithms. In addition to complexity analysis, these results indicate a global optimum solution, an effective representation of least amount of data-high dimensionality reduction and an avoidance of over-fitting problems.

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A new feature selection algorithm based on binary ant colony optimization

Cited by 43 publications

References 9 publications

Metaheuristics for feature selection in handwritten digit recognition

Metaheuristics for feature selection in handwritten digit recognition

A hybrid feature selection method for high-dimensional data

Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

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